DESIGN AND EVALUATION OF BRT AND LIMITED-STOP SERVICES
By
Harvey Scorcia
B.S. Civil Engineering, Universidad de los Andes (2005)
B.A. Music, Universidad de los Andes (2006)
M.S. Civil Engineering, Universidad de los Andes (2006)
Submitted to the Department of Urban Studies and Planning and the Department of Civil and
Environmental Engineering in partial fulfillment of the requirements for the degrees of
Master in City Planning
and
Master of Science in Transportation
at the
Massachusetts Institute of Technology
June 2010
© Massachusetts Institute of Technology. All rights reserved
MASACHUSETTS INSTIUTE
OF TECHNOLOGY
JUL 15 2010
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ARCHIVES
Signature of the Authui
Department of Urban Studies and Planning
Department of Civil and Environmental Engineering
May 24, 2010
Certified by
Nigel H.M. Wilson
Professor of Civil and Environmental Engineering
Thesis Supervisor
Certified by
Researc
(
John Attanucci
<iate of Civil and Environmental Engineering
Thesis Supervisor
Accepted by
Joseph Ferreira
//
Chairman, Master in City Planning Committee
07egment 9 Urjp tdies and Planning
Accepted by
Daniele Veneziano
Chairman, Departmental Committee for Graduate Students
DESIGN AND EVALUATION OF BRT AND LIMITED-STOP SERVICES
By
Harvey Scorcia
Submitted to the Department of Urban Studies and Planning and the
Department of Civil and Environmental Engineering
on May 20, 2010, in partial fulfillment of the requirements for the degrees of
Master in City Planning and Master of Science in Transportation
Abstract
Many transit agencies operate limited-stop or Bus Rapid Transit (BRT) services overlapped with
local services in corridors with high demand. These service strategies have the potential to
improve bus performance as well as service quality. However, the implementation of these
service strategies may lead to an increase in access times and waiting times for some passengers
compared with an all-local service configuration. Therefore, agencies face trade-offs between
reducing bus running times and reducing total passenger travel time when designing a service
plan for these overlapping strategies.
This thesis focuses on developing a methodology for the design and evaluation of service
configurations for limited-stop (or BRT) services overlapping with local services. The developed
methodology proposes evaluating limited-stop (or BRT) service configurations by six measures
of effectiveness including: market share (passengers always waiting for the limited-stop service,
passengers always waiting for the local service, and passengers always taking the first bus that
comes), demand split, average passengers per trip, service running times, change in average
passenger travel time, and change in corridor ridership.
A model was developed to obtain the proposed measures of effectiveness for user-defined
service configurations. This model is an improvement to that developed by Schwarcz (2004)
since it allows forecasting ridership changes due to the implementation of these service
configurations, assigns demand using a probabilistic choice approach, and models running time
changes when BRT elements are introduced.
The methodology developed to evaluate limited-stop and BRT services was applied to two CTA
case studies: Chicago Avenue and 7 9 th Street. Different scenarios of limited-stop and BRT
services overlapped with local services were tested, examining variations in stop spacing, service
frequencies, and different BRT elements such as: right-of-way segregation, enhanced boarding,
and Transit Signal Priority. The results of the analysis shows the critical importance of the
enforcement of preferential rights-of-way (in BRT scenarios) to achieve high corridor
performance and that frequency shares (the ratio of limit-stop services buses to local buses
serving the corridor in an hour) should be greater than 50% for limited-stop services and greater
than or equal to 60% for BRT services. Additionally, total demand, concentration of origins and
destinations, average trip length, and trip length distribution were shown to be critical to the
effectiveness of limited-stop and BRT services.
Thesis Supervisor: Nigel H.N. Wilson
Title: Professor of Civil and Environmental Engineering
Thesis Supervisor: John Attanucci
Title: Research Associate of Civil and Environmental Engineering
Thesis Reader: Christopher Zegras
Title: Assistant Professor of Urban Studies and Planning
ACKNOWLEDGEMENTS
This thesis is the culmination of 3 challenging years of my life. Coming to MIT was an
experience that taught me to be a better person and to appreciate things I took for granted such as
family, love and friendship. I have countless people who I want to thank:
First, I would like to thank to my family for the unconditional support I have received during
these years. There was no time that I felt I could not count with my mom Elsa, my dad Augusto,
or my sister Yiya.
Second, I offer my gratitude to the MIT faculty members who work with me. I want to thank
Professor Nigel Wilson for guiding me through my research always pushing me towards
excellence, to John Attanucci and Professor Rabi Mishalani for their guidance during the
research process. Other faculty members I would like to thank for encourage me to come to MIT
are German Lleras, Arturo Ardila, and Jorge Acevedo. Also, thanks to the Chicago Transit
Authority for making this research possible.
Third, I would like to thanks my 1-235 colleagues including Sean, Hazem, Catherine, DCBS,
Martin, Liz, Valerie, Andrew, Winnie, Mike F., Mike H., Mike K., Clara, Yossi, Caroline, Matt,
Nihit, Jay, Joe, Albert, Sam, and Miguel. Very special thanks to Tony and Jared for helping
carrying out surveys in Chicago during Spring Break and to Ginny Siggia for her helping me
with the administrative work. I cannot forget thanking my DUSP friends Renata and Andrea.
Fourth, I would like to thank to Candy Brakewood for the unconditional help in Boston,
Chicago, and London; thanks to Andre for the good times helping me to survive my second year;
thanks to Carlos Mojica for all the provided help and listening to all my complaints about MIT
during the first year; thanks to David Uniman for all the good times and always reminding me
that MIT was just a small period of my life; thanks to Juliin G6mez for always laughing about
my bad jokes; and thanks to Felipe Delgado for showing me Boston and remembering me there
was life outside 1-235.
Fifth, I thank to the people who were in Boston during this 3 years and that I could count with
when need it including Tomis, Sandra, William, and Alvaro
Sixth, I would like to thank my Colombian friends who always support me: Pablo Pirraga,
Carlos Diaz, Oscar Torres, Juan Diego Villalobos, Walther Garcia, Juan Carlos Rodriguez, and
Claudia Diaz.
Last, I would like to thank Andrea Murcia for her love and patience during these years. I am very
happy for being at this moment and I am looking forward to spend a long time together.
Today, this is the end of a period of my life but "far from being the end, this is just the
beginning" (Gomez, 2010).
TABLE OF CONTENTS
TA BLE O F CONTEN TS............................................................................................................................................6
LIST O F FIG URES.....................................................................................................................................................9
LIST O F TABLES .....................................................................................................................................................
11
1
INTROD U CTIO N ...........................................................................................................................................
13
1.1
M OTIVATION.............................................................................................................................................
14
1.2
OBJECTIVES ..............................................................................................................................................
15
1.3
M ETHODOLOGY AND APPROACH ..............................................................................................................
16
1.4
THESIS ORGANIZATION .............................................................................................................................
17
LITERATUR E REV IEW ...............................................................................................................................
19
2
2.1
MODELING AND EVALUATING LIMITED-STOP SERVICE: THE SCHWARCZ MODEL ................................
19
2.1.1
Model Description ...............................................................................................................................
19
2.1.2
Model validation..................................................................................................................................
21
2.1.3
CriticalAssessm ent .............................................................................................................................
22
2.2
RIDERSHIP ESTIM ATION ............................................................................................................................
23
2.3
PASSENGER SERVICE CHOICE ...................................................................................................................
27
2.4
RUNNING TIM ES........................................................................................................................................28
2.4.1
Right-of-way........................................................................................................................................28
2.4.2
Transit Signal Priority(TSP) ..............................................................................................................
29
2.4.3
Queue jumps/Bypass lanes ..................................................................................................................
30
2.4.4
Vehicle design andfare collection...................................................................................................
31
2.4.5
Stop spacing.........................................................................................................................................
32
2.5
........... 32
2.5.1
Transmilenio........................................................................................................................................
32
2.5.2
Los Angeles County Metropolitan TransportationAuthority (LACMTA): ...................
37
2.6
3
EXPERIENCES IN CITIES WITH CONVENTIONAL LIMITED-STOP AND BRT SERVICE.........
SUMMARY
.................................................................................
PREDICTING RID ERSH IP CH A N GES ...................................................................................................
3.1
PROPOSED M ETHODOLOGY.......................................................................................................................44
3.2
MEASURING TRAVEL TIME ELASTICITIES IN THE ASHLAND CORRIDOR IN CHICAGO.............................45
3.2.1
Service changes on the corridor......................................................................................................
3.2.2
Ridership Changes...............................................................................................................................48
42
43
45
3.2.3
Changes in Passenger Travel Time .................................................................................................
52
3.2.4
Travel Tim e Elasticities.......................................................................................................................
57
3.3
RIDERSHIP CHANGE WHEN IMPLEMENTING BRT LIMITED-STOP SERVICES........
3.4
SUM M ARY ...................................................-------
....---------.----.
59
..........................
60
.........................................................
62
BUS SERVICE CHOICE: UNDERSTANDING PASSENGER BEHAVIOR ...................
4
62
4.1
CONCEPTUAL FRAM EW ORK ......................................................................--.-.-.-.-.--.-......
.........................
4.2
PROPOSED MODELING APPROACH...............................................................
............
...... 65
4.3
CTA CUSTOMER BEHAVIOR SURVEY ........................................................................
73
Survey P rocess.. ............................................................................................
4.3.2
Field Observations................................................................................................-
4.3.3
Data A nalysis.... .............................................................................
D ISCRETE CHOICE M ODEL ...........................................................-
4.4
73
....................................
4.3.1
... -............
. --.74
75
........... ............................
...
---........
----.
. .............................. 82
PROPOSED METHODOLOGY.................................................................-------------------------------...................85
5
-..... --..... -------.....
M ODEL A PPROACH .....................................................-
5.2
M ODEL FRAM EW ORK ...........................................................-----------.-.....---...-------..................................
5.3
M ODEL INPUTS......................................................................----
5.4
RUNNING TIME ESTIMATION............................................................................-......-.....
..... -
85
-----...........................................
5.1
87
8 8.
.---------.--.................................
90
..------..
5.4.1
Estimate base running times by type .....................................................................................
90
5.4.2
Compute average m ovem ent speed..................................................................................................
91
5.4.3
Dw ell tim e calibration.........................................................................................................................
92
5.4.4
Estim ate change in running tim e ......................................................................................................
92
5.5
ORIGIN-DESTINATION (OD) DEMAND MATRIX ESTIMATION.................................................................95
5.6
PASSENGER DEMAND ASSIGNMENT AND ESTIMATION OF DEMAND CHANGES..........................................95
5.6.1
Passengertravel time estim ation......................................................................................................
96
5.6 2
98
5.6.3
Market classification...........................................................................................................................
......
Service and Stop Assignment............................................................................................
.. 99
5.6.4
Example of market classificationand demand assignment .............................................................
99
5.6.5
P redictingchanges in ridership......................................................................................................
101
MODEL OUTPUTS: MEASURES FOR EVALUATING LIMITED-STOP SERVICE CONFIGURATIONS ................ 103
5.7
5 .7.1
Market Sh are .....................................................................................................................................
10 4
5 .7.2
D em an d Sp lit .....................................................................................................................................
104
5.7.3
A verage Passengersper Bus Trip.....................................................................................................104
5.7.4
Running Time Savings/Changes on Speeds ........................................................................ 104
5.7.5
Change in Average PassengerTravel Tim e ......................................................................................
105
5.7.6
Chang e inRidership ..........................................................................................................................
105
5.8
SUM M ARY ........................................................................
. -------...
.
----------------------....................................
105
7
6
MODEL APPLICATION .............................................................................................................................
6.1
CHICAGO CORRIDOR APPLICATION.........................................................................................................
106
106
6.1.1
CorridorDescription .........................................................................................................................
106
6.1.2
Scenario 1. ConventionalLimited-Stop..............................
110
6.1.3
Scenario 2: Moderate BRT enhancements...................................................113
6.1.4
Scenario 3 . F ull BR T .........................................................................................................................
6.1.5
Summary of Chicago Avenue CorridorFindings
6.2
APPLICATION:
7 9
TH
...........
............................
117
........... 121
STREET CORRIDOR ...................................................................................................
122
6.2.1
CorridorDescription .........................................................................................................................
122
6.2.2
Scenario 1: Conventional Limited-Stop..... ....................................
125
6.2.3
Scenario 2: ModerateBR T enhancements........................................128
6.2.4
Scenario 3: Full BR T .........................................................................................................................
133
6.2.5
Summary of 79 'hCorridorFindings.....
137
7
......... ..............................
CONCLUSIONS AND RECOMENDATIONS...........................................................................................139
7 .1
S UM M A R Y ...............................................................................................................................................
13 9
7.2
GENERAL RECOMMENDATIONS...............................................................................................................
143
7.2.1
Corridors/Routeswith Potentialfor introducingBRT and/or Limited-Stop Services ....................... 143
7.2.2
BRT and Limited-Stop Service ConfigurationDesign.......................................................................145
7.3
C T A RECOM M EN DATION S ......................................................................................................................
148
7.4
M OD EL L IM ITA TIO N S ..............................................................................................................................
150
7.5
FUTU RE R ESEA RCH .................................................................................................................................
15 1
BIBLIOGRAPHY ...................................................................................................................................................
153
APPENDIX A ..........................................................................................................................................................
155
APPENDIX B...........................................................................................................................................................158
8
LIST OF FIGURES
Figure 2-1 Conceptual framework of the Schwarcz model ......................................................
Figure 2-2 Transmilenio System................................................................................................
Figure 2-3 Articulated and Bi-Articulated Transmilenio Buses ................................................
Figure 2-4 Transmilenio Right-of-Way....................................................................................
Figure 2-5 Transm ilenio station................................................................................................
Figure 2-6 Boarding and ticket validation processes................................................................
Figure 2-7 M etro Local bus ......................................................................................................
Figure 2-8 Metro Rapid System................................................................................................
Figure 2-9 Metro Rapid bus and station ....................................................................................
Figure 2-10 Los Angeles Orange Line.......................................................................................
Figure 2-11 Los Angeles Orange Line bus................................................................................
Figure 2-12 Overall customer rating of different transit modes in LA vs. Capital Cost ..........
Figure 3-1 CTA Routes 9 and X9.............................................................................................
Figure 3-2 Combined headways before and after the implementation of Route X9 .................
Figure 3-3 Average Daily Boardings Route 9/X9 ....................................................................
Figure 3-4 R oute 9/X 9 ridership ................................................................................................
Figure 3-5 Route 9/X9 trip length histograms ...........................................................................
Figure 3-6 Route 9/X9 ridership change by trip length, trip period and direction ....................
Figure 3-7 Headway variation before and after implementation of Route X9 ..........................
Figure 3-8 Speed profiles before and after implementation of Route X9..................................
Figure 3-9 Changes in travel time weighted by demand ...........................................................
Figure 3-10 Travel Time Elasticities by Trip Length ................................................................
Figure 3-11 Average Travel Time Elasticities by Trip Length..................................................
Figure 4-1 Multinomial Logit Model.........................................................................................
Figure 4-2 Cross-Nested Logit Model......................................................................................
Figure 4-3 Alternative Cross-Nested Logit Model Structures..................................................
Figure 4-4 Example of Path-size definition.............................................................................
Figure 4-5 Access Time Diagram.............................................................................................
Figure 4-6 In-Vehicle Time Diagram .........................................................................................
Figure 4-7 In-Vehicle Time for the Local Strategy ..................................................................
Figure 4-8 Egress Time Diagram.............................................................................................
Figure 4-9 Frequency of use and trip purpose during the morning in the Ashland and Cicero
co rrid ors ........................................................................................................................................
Figure 4-10 Strategy by local service passengers boarding at a local stop................................
Figure 4-11 Strategy by local service passengers boarding at a combined stop.......................
Figure 4-12 Strategy by limited-stop passengers boarding at a combined stop .......................
Figure 4-13 Demand Split between services and stops .............................................................
Figure 4-14 Market shares in the Ashland and Cicero Corridors .............................................
Figure 4-15 Demand split of local service passengers boarding at a local stop (N=39) ...........
Figure 4-16 Demand split of local service passengers boarding at a combined stop (N=8 1) ......
Figure 4-17 Demand split of limited-stop service passengers boarding at a combined stop
(N=4 1)...........................................................................................................................................
Figure 5-1 M odel Fram ew ork....................................................................................................
Figure 5-2 Bus Travel Time Components ..................................................................................
20
33
34
35
35
36
37
38
39
40
41
42
46
47
48
49
51
52
54
55
57
58
59
64
64
65
67
68
70
70
72
76
77
77
77
78
79
80
80
80
88
91
Figure 5-3 Market Classification Example...............................................................................
99
Figure 6-1 C TA R oute 66 ...........................................................................................................
107
Figure 6-2 Route 66 Trip Length Distribution............................................................................
108
Figure 6-3 Cumulative AM Peak Eastbound demand for Route 66 ....................
109
Figure 6-4 Running Time Components for Route 66 in the AM Peak eastbound.......... 109
Figure 6-5 Change in travel time by trip length for the limited-stop service only configuration for
R ou te 6 6 ......................................................................................................................................
112
Figure 6-6 Change in travel time by trip length for the moderate BRT only service configuration
.....................................................................................................................................................
115
Figure 6-7 R oute 66 Load Profile ...............................................................................................
116
Figure 6-8 Change in travel time by trip length for the full BRT only configuration ................ 120
Figure 6-9 C TA Route 79 ...........................................................................................................
123
Figure 6-10 Route 79 Trip Length Distribution..........................................................................
124
Figure 6-11 Cumulative AM Peak Westbound demand for Route 79..................
124
Figure 6-12 Running Time Components for Route 79 in the AM Peak westbound......... 125
Figure 6-13 Change in travel time by trip length for the limited-stop only service configuration
for R ou te 6 6 ................................................................................................................................
12 7
Figure 6-14 Change in travel time by trip length for the moderate BRT only service
co nfigu ration ...............................................................................................................................
13 1
Figure 6-15 Route 79 Load Profile .............................................................................................
132
Figure 6-16 Change in travel time by trip length for the full BRT only service configuration.. 136
LIST OF TABLES
25
Table 2-1 Typical service elasticities.........................................................................................
26
Table 2-2 Additional ridership impacts of selected BRT components ......................................
29
Lanes..................................
HOV
Freeway
Exclusive
and
Busways
on
Table 2-3 Bus Speeds
29
Table 2-4 Bus Speeds on Dedicated Arterials ...........................................................................
29
Table 2-5 Bus Speeds in General Purpose Traffic Lanes .........................................................
Table 2-6 Passenger service times with single-channel passenger movement......................... 31
Table 2-7 Passenger service times with multiple-channel passenger movement ..................... 31
32
Table 2-8 Base Running Speeds for Dedicated Arterial Bus Lanes .........................................
47
Table 3-1 Headways on Route 9 and X9 ..................................................................................
48
Table 3-2 Frequency split between Routes 9 and X9 ...............................................................
50
Table 3-3 Route 9/X 9 ridership split ........................................................................................
50
Table 3-4 Route 9/X9 average trip lengths (miles)..................................................................
54
Table 3-5 Headway Variability for Routes 9 and X9 ...............................................................
55
..........................
X9
Route
of
Table 3-6 Average Speeds before and after the implementation
57
Table 3-7 Average Travel before and after Route X9 implementation ....................................
60
......................................
components
BRT
Table 3-8 Additional ridership impacts of selected
75
Table 4-1 Num ber of surveys by type.......................................................................................
84
Table 4-2 Param eters of the logit model....................................................................................
92
Table 5-1 Passenger service times with single-channel passenger movement .........................
93
......................................................
Lanes
Bus
Exclusive
for
Table 5-2 Base Running Times
Table 5-3 Passenger service times with multiple-channel passenger movement ..................... 94
Table 5-4 Probabilities, access time, egress time, waiting time, and in-vehicle time ................ 101
101
Table 5-5 Passenger Travel Time Elasticities in Chicago ..........................................................
103
...............................
to
Branding
due
ridership
additional
the
of
estimating
5-6
Example
Table
LimitedTable 6-1 Running times components in the Chicago Corridor Scenario 1: Conventional
.......... ---.....- . 1 10
Stop S erv ice .......................................................................................................
Table 6-2 Chicago Corridor Scenario 1: Conventional Limited-Stop Service........................... 111
Table 6-3 Running times components in the Chicago Corridor Scenario 2: Moderate BRT
..... ----............... 113
Serv ices ......................................................................................................114
Table 6-4 Chicago Corridor Scenario 2: Moderate BRT Services .............................................
BRT
Moderate
demand:
new
of
the
effect
Table 6-5 Chicago Corridor Scenario 2 including
.... .............. 116
. . . . ................................................................................
Services .............
Services 118
BRT
Full
3:
Scenario
Corridor
Chicago
in
the
components
times
Table 6-6 Running
119
Table 6-7 Chicago Corridor Scenario 3: Full BRT Services ......................................................
Table 6-8 Chicago Corridor Scenario 3 including the effect of new demand: Full BRT Services
- - -- - - . 121
. . . . ...........................................................................................................
Table 6-9 Running times components in the 79th Street Corridor Scenario 1: Conventional
126
L imited-Stop Service ..................................................................................................................
Table 6-10 7 9 th Street Corridor Scenario 1: Conventional Limited-Stop Service...................... 127
Table 6-11 Running times components in the 7 9 th Street Corridor Scenario 2: Moderate BRT
. --- .. . . . -------............... 12 9
Serv ices .............................................................................................
Table 6-12 7 9 th Street Corridor Scenario 2: Moderate BRT Services ........................................ 130
Table 6-13 7 9 th Corridor Scenario 2 including the effect of new demand: Moderate BRT Services
132
....................................................................................................
Table 6-14 Running times components in the 7 9 th Street Corridor Scenario 3: Full BRT Services
.....................................................................................................................................................
134
Table 6-15 7 9 th Street Corridor Scenario 3: Full BRT Services .................................................
135
Table 6-16 7 9 th Corridor Scenario 3 including the effect of new demand: Full BRT Services.. 137
1
INTRODUCTION
Conventional bus services in the largest US cities are characterized by having little or no special
infrastructure, low quality waiting areas, no distinct image, and high stop densities. These
characteristics often lead to poor performance with slow service, low reliability, and low
productivity that has become more severe as congestion has worsened in large cities.
In order to improve bus performance, enhance bus image, and increase ridership, transit agencies
can implement different strategies. One no-cost strategy is to increase bus stop spacing; however,
for political reasons this is often unacceptable since it will negatively affect people with
disabilities or other mobility constraints. A second strategy is the implementation of limited-stop
services overlapping with existing local services. A third, more costly, strategy is the
implementation of Bus Rapid Transit' (BRT) services. There are two options of this latter
strategy. The first is to discontinue the existing local service and replace it with a single BRT
service. The second is to maintain local service but with a much lower frequency and implement
an overlapping BRT service on the same corridor.
This thesis focuses on developing a methodology for the design and evaluation of service
configurations for limited-stop services overlapping with local services, and BRT services
overlapping with local services. These two strategies have the potential to improve bus
performance as well as passengers' perception of bus while still serving people with restricted
mobility. Despite the fact that both of these service strategies have been implemented in cities
such as Chicago, Los Angeles, New York, and Bogota, and that transit agencies and consultants
have developed guidelines on how to implement them, there is no clear framework for the design
and evaluation of service configurations for these types of high quality bus services.
Bus Rapid Transit (BRT) is defined as "a rubber-tired form of rapid transit that combines stations, vehicles,
services, running ways, and ITS elements into an integrated system with a strong image and identity". TCRP Report
90. Bus Rapid Transit. Volume 1: Case Studies in Bus Rapid Transit (2003)
One key problem with the design of these services, especially in the context of a constrained
operating budget where the existing local service resources are split between the limited-stop
service and the local service, is the trade-off between decreasing in-vehicle travel time (by
increasing speeds) and increasing out-of-vehicle travel time (by increasing wait times and walk
distances). For example, a low stop density configuration can reduce the in-vehicle time (and the
running time), leading to a fleet size reduction; but, at the same time, this configuration could
increase the access and egress time, to the point that the overall passenger travel time is
increased with respect to the base case. Another design challenge with these services is the
possible passenger load imbalance between the local and the limited-stop (or BRT) services. For
instance, low frequencies on the limited-stop (or BRT) service can encourage passengers to take
the local service leading to more crowded local buses and half-empty BRT buses. In addition,
when planning BRT services, transit agencies need to assess the effect on route performance
(and attractiveness for new riders) of variables such as vehicle size, right of way, transit signal
priority, and boarding enhancements.
To address the aforementioned problems, this thesis has as its primary goal to establish a
methodology for the design and evaluation of service configurations of limited-stop and BRT
services which overlap with existing local services. The configuration and BRT elements studied
in this thesis are: bus stop locations (stop spacing), service frequencies (for the limited-stop or
BRT service and for the local service), right-of-way (lane segregation level), enhanced boarding
elements (i.e. fare media and bus design), and Transit Signal Priority (TSP).
In order to assess different service configurations in a corridor, a model which estimates (for a
single configuration) measures of effectiveness (demand split between services, average
passengers per trip for both services, change in passengers travel time, service speed or running
times, and change in corridor/route demand) will be developed. The model will be largely based
on the one developed by Schwarcz (2004).
1.1
Motivation
Many transit agencies in the US operate both limited-stop and local bus service in corridors with
high demand. The combined services have the potential to benefit both the agency and the riders
by reducing passenger travel times and operator cost. However, in a budget-constrained
resource-neutral strategy, the implementation of a strategy with both services leads to an increase
in access times and waiting times for some passengers compared with an all-local service
configuration. Therefore, agencies face trade-offs between reducing bus running times and
reducing total passenger travel time when designing a service plan for this strategy. Different
configurations (varying bus stop densities, vehicle types, frequencies, fare technology, etc.) will
result in different operational and infrastructure costs for the agency, as well as in different levels
of service for the passengers. The methodology proposed in this thesis will help transit agencies
in developing and evaluating service configurations for new or existing limited-stop services
overlapping with local services.
In addition, many transit agencies in the US are implementing or considering new BRT services.
Some of these are the result of an evolution from conventional limited-stop services, and others
are simply improvements to conventional local bus services. When a BRT service is
implemented, there is often a focus on measuring its success by the increase in vehicle operating
speeds, with less emphasis on other important measures such as passenger travel time savings,
changes in productivity, or passenger distribution between local and BRT service. The
methodology to design and evaluate limited-stop service configurations developed in this thesis
will help provide a more balanced assessment of proposed service configurations based on more
than one measure of effectiveness.
1.2
Objectives
There are two primary objectives of this thesis. The first is to develop a methodology for the
design and evaluation of configurations of limited-stop and BRT services overlapping with local
services. The configuration elements within the scope of this thesis are: bus stops locations (stop
spacing), service frequencies (for the limited-stop or BRT service and for the local service),
right-of-way (lane segregation level), enhanced boarding elements (i.e. fare media and bus
design), and Transit Signal Priority (TSP). The second is to apply the methodology to specific
routes operated by the Chicago Transit Authority (CTA), examining several configurations, to
develop some general recommendations for the design of limited-stop and BRT services, and to
provide specific recommendations to the CTA regarding their strategy to implement and design
BRT and limited-stop services.
This thesis addresses the following specific questions:
" How to model the relationship between the service configuration elements and the
measures of effectiveness
* How the key BRT elements, the stop spacing, and the service frequency split affect route
performance
*
How to model the changes in ridership when limited-stop or BRT services are introduced
" How passengers choose between local and limited-stop (or BRT) services
1.3
Methodology and Approach
This thesis will be based on a model developed by Schwarcz (2004). This model has certain
limitations such as the inability to predict changes in demand as a result of the implementation of
limited-stop services, a deterministic approach for assigning passenger demand (between the
limited-stop and the local services) which does not recognize that individual passengers have
different preferences, and an inability to model BRT services.
For the past decade or so, the CTA has operated numerous limited-stop services. The available
information at the CTA largely from Automatic Vehicle Location (AVL) and Automatic
Passenger Counting (APC) systems provides detailed information on the changes (i.e. demand,
running times, passenger travel times, etc.) in the corridors where limited-stop service has been
implemented in addition to providing base data for other corridors where limited-stop or BRT
services could be implemented in the future.
To accomplish the objectives of this thesis, the following steps are necessary:
e Review priorresearch on modeling limited-stop services. This step reviews the state-ofthe-art of modeling limited-stop and BRT services, and examines the areas where the
previous model requires improvements such as the prediction of ridership changes due to
the implementation of limited-stop and BRT services, the modeling of passenger service
choices, and the modeling of running times including the effect of BRT elements.
e Develop a methodologyfor examining andpredictingchanges in demand. Based on the
experience and data from the CTA and the literature review, this step proposes a
methodology to examine the relationship between changes in ridership and changes in
travel times (including access, egress, waiting and in-vehicle time), and the effects of
branding.
e Develop a methodologyfor modeling the passengerchoices between limited-stop (or
BRT) services and local services in the same corridor.A discrete choice model is
developed for Chicago corridors based on the experience and data from the CTA, and a
survey carried out on several limited-stop and local services in Chicago.
* Develop a new model to design and evaluate service configurations of limited-stop (or
BRT) services overlappingwith local service. This model evaluates a user-defined service
configuration: meaning that the local and limited-stop frequencies and stops are specified,
as well as the BRT elements (right-of-way, enhanced boarding and Transit Signal
Priority). The model calculates several measures of effectiveness (based on comparison
with the existing local service) including demand split between services, productivity for
both services, and changes in passengers travel time, service speed (running times), and
corridor/route demand.
e Apply the model to CTA case studies and establish some generalrecommendationsfor
the design of limited-stop and BRT services. This step interprets the effects of the service
configuration elements (stop spacing, service frequencies, etc.) on corridor performance
using the measures of effectiveness. Several configurations are examined including a
sensitivity analysis of the different configurations and BRT elements.
1.4
Thesis Organization
This thesis is organized as follows:
Chapter 2 reviews and critiques the Schwarcz model to evaluate limited-stop services, presents a
review of the state-of-the-art in the areas where the model needs improvement (including
ridership prediction and developing a probabilistic approach for assigning demand between local
and limited-stop services), and reviews several cities experiences with limited-stop and BRT
services.
Chapter 3 develops a methodology for assessing and predicting changes in demand when
conventional limited-stop and BRT services overlapping with local services are introduced. The
chapter explores the relationships between changes in passenger travel time (including access
time, waiting time, and in-vehicle time) and changes in ridership, based on the experience of
limited-stop services in Chicago. The chapter also proposes a methodology for accounting for the
branding effects based on the literature review.
Chapter 4 presents a methodology for developing a discrete-choice model for passengers
choosing between local and limited-stop (or BRT) services. A specific discrete-choice model is
developed for Chicago based on the CTA's AVL and APC data, and a survey carried out during
spring 2009 in Chicago. The chapter also summarizes the survey results, and the field
observations.
Chapter 5 describes the enhanced limited-stop and BRT planning model. The new model predicts
measures of effectiveness for a specific service configuration of limited-stop (or BRT) service
overlapping with local service: the demand split between services, the productivity for both
services, and the changes in passengers travel time including in-vehicle and out-of-vehicle time,
services speed (running times), and demand.
Chapter 6 applies the model to the Chicago Avenue corridor and the
7 9
th
street corridor in the
city of Chicago. Several configurations are modeled including limited-stop services overlapping
with local services, BRT services (with different levels of lane segregation, enhanced boarding,
and TSP) overlapping with local services, and variations of stop spacing and service frequencies.
Chapter 7 summarizes the thesis findings, presents general recommendations for the design of
limited-stop and BRT services overlapping with local services, including the impact of the
different service configuration elements (stop spacing, service frequencies, right-of-way,
enhanced boarding, and TSP), provides recommendations for the CTA, and presents suggestions
for future research on this topic.
2
LITERATURE REVIEW
This chapter is composed of five sections. The first summarizes the methodology proposed by
Schwarcz (2004) to evaluate limited-stop bus service configurations and discuses its limitations.
The second section reviews the state-of-the-art of how to estimate ridership for conventional
limited-stop and BRT limited-stop services. The third section examines the literature in how to
model passenger choices when limited-stop services overlap with local services. The fourth
section summarizes the literature regarding modeling running times for limited-stop and BRT
services. The fifth section reviews several cities' experiences with the aforementioned services.
2.1
Modeling and Evaluating Limited-Stop Service: The Schwarcz Model
Schwarcz (2004) developed a descriptive model to evaluate limited-stop service that assesses
future corridor performance based on the current local service and the proposed future service
configuration. The performance of a specific proposed configuration is measured in terms of the
market share2, the demand split between the local and the limited-stop service, the change in
passenger's travel time, and the change in productivity.
2.1.1
Model Description
Figure 2-1 presents the basic framework of the Schwarcz model. The model inputs include, for
an specific time period and direction: the resources (number of buses and platform hours), the
frequencies of both services, the current and proposed stop locations, the distance between stops,
the demand at the stop level, the running times, and the travel time component "weights" (i.e. the
relative importance of the various components of trip travel time -walking, waiting, and
travelling on-board the bus- to typical passengers).
This measure refers to the percentage of passengers that choose to wait for the limited-stop service, the percentage
that choose to wait for the local service, and the percentage that choose to take whichever bus come first.
2
Assignment
1. Compute Travel Time
components (Access + Waiting
+ In vehicle) for each OD pair,
for three possibilities (Local,
Limited, and First Bus)
2.Market Classification. For each
OD pair take the min. travel
time of the 3 cases
3. Assignment.
" Stop assignment. Number of
passengers per stop
- Route assignment. Number of
local and limited service
passengers on
Figure 2-1 Conceptual framework of the Schwarcz model
The model starts by estimating of the Origin-Destination (OD) matrix of the existing local
service. Unless an actual OD matrix is known from a detailed passenger survey, this matrix is
estimated based on boardings and alightings counts at the stop level using Iterative Proportional
Fitting (IPF) and the methodology proposed by Navick and Furth (1994).
Once the OD matrix is estimated, the model performs the passenger assignment process in three
steps. In the first step, the model computes the weighted travel time of every OD combination for
three strategies: a) the passenger waits for the local service (local preferred), b) the passenger
waits for the limited-stop service (limited preferred), and c) the passenger takes the first bus that
arrives (choice) at a particular stop. The model assumes no access and egress time for any
existing passengers travelling between proposed combined stops (stops served by both local and
limited-stop buses) and estimates the extra walking (access and egress) times for the passengers
boarding and/or alighting at the local-only stops. The waiting times are estimated based on the
proposed frequencies and on the expected headway coefficient of variation. The in-vehicle times
are estimated based on the distances traveled on the bus and the projected speeds. Equations
(2-1), (2-2), and (2-3) show how the weighted travel times are estimated for the three possible
strategies.
TTLOC =WTLoc -W7T
+IVTLoc
TTLi, =[A Togi + A TDest ]W
(2-1)
-W
+WT Lim
±
TTcloice= A To,,, + (F)-A TDestJ. WAT
+
*WWT
+
WTom -w+
(2-2)
IVTLin - WIV
[(1 - F). IVTChoiceLoc + (F)-IVTLim]-W
4
(2-3)
Where: TTLOc, TTLim, TTChoice are the total weighted travel times for local preferred, limited
preferred, and choice passengers respectively
WAT, WwT, WIVT are the travel time weights for access, wait, and in-vehicle time
respectively. The model accounts for the fact that passengers perceive out-of-vehicle
times to be more onerous that in-vehicle time. The suggested weights by Schwarcz are
2.5 for access time, 2 for waiting time, and 1 for in-vehicle time
A Torigin, A TDest, are the origin and destination access times respectively
WTLoc, WTLim, WTCom are the local, limited-stop, and combined service expected wait
times respectively
IVTLoc, IVTLim, IVTChoiceLoc, are the local, limited, and ChoiceLoc in-vehicle times
respectively (ChoiceLoc is the in-vehicle time on the local service from the nearest
limited-service stop)
F, is the frequency share (the percentage of total bus trips that are provided on the
limited-stop service)
In the second step, the model assigns a market share for each OD pair based on the alternative
with the minimum weighted travel time (local preferred, limited preferred, or choice). In the
third step, the model assigns the current passengers to the bus stops and to the route (local or
limited-stop) based on the market share established in the second step and the frequency share.
The results of the second and third steps are aggregated to estimate measures of effectiveness of
the proposed service configuration including: the market share (the total amount of passengers in
each of the three strategies), the demand split (number of passengers taking each service), the
change in average passenger travel time, and the productivity of each service
(passengers/platform hours).
2.1.2
Model validation
The Schwarcz model was validated by Scorcia using the data from the Ashland Avenue corridor
in the city of Chicago where a limited-stop service (Route X9) was introduced overlapping the
local service (Route 9) in summer 2006. The data collected before the implementation of the
service was used to predict the change in passenger travel time, services speeds, productivity
(passengers/platform hour), market classification, and demand split between routes and
compared all these predictions with the actual numbers after 18 months of implementation.
The validation process showed that the predictions of the model were accurate for the demand
split, the service speeds, and the productivity. However, the market share prediction could not be
validated since it requires a survey where passengers are asked whether they wait for the limitedstop service, wait for the local service, or take the first bus that comes. Route 9/X9 also showed
ridership increases above the system average and increases in reliability that were not predictable
using the Schwarz model.
2.1.3
Critical Assessment
The Schwarcz model has several limitations. The first is that the model does not predict any
change in demand when limited-stop service is implemented. However, transit agencies
(including NYC Transit, Chicago Transit Authority, and Metro Rapid in Los Angeles) have
found that the introduction of limited-stop service often results in ridership increases. BRT
services in North America have also proven to be successful in attracting new ridership. For
instance, the experience of six major urban areas, where BRT was implemented, including Los
Angeles, Miami, Brisbane (Australia), Vancouver (BC), Boston, and Oakland have shown that
"corridor ridership has grown faster than the reduction in transit travel time, suggesting
demand/travel time elasticities over 1.O."(TCRP Report 118, 2007)
The second limitation is that the Schwarcz model assumes that all passengers for a given OD
choose the strategy (local preferred, limited preferred, or choice) that minimizes their weighted
travel time. In other words, the model performs an all-or-nothing market share assignment.
However, while the model seems to give reasonable results, it is well known that individual
passengers' decisions are not based only on travel time but are also influenced by many other
variables such as socioeconomic characteristics, age, weather conditions, availability of shelters
at stops, real time information, trip purpose, and lack of awareness about the limited-stop service.
Finally, other limitations of the Schwarcz model are:
"
The model was designed for limited-stop services and not for BRT services.
" Running times for proposed limited-stop services are estimated with a simple approach
based solely on the number of skipped stops.
" It does not account for the effects of branding on ridership changes and on passengers'
service choice behavior.
" The model can only test one specific configuration at a time.
*
It is a descriptive rather than an optimization model.
This thesis is focused on addressing some of these limitations, specifically by estimating
ridership changes, including a probabilistic approach for assigning demand, and allowing one to
evaluate not only limited-stop services but also BRT services.
2.2
Ridership Estimation
Conventional limited-stop and BRT services have been successful in attracting new ridership in
the US. Nonetheless, there is little prior research on ridership estimation for these services. The
information available in the literature is not exclusive to how limited-stop services influence
demand but rather discusses how general changes in service characteristics (i.e. fares, travel
times, hours of service, and frequencies) affect ridership.
TCRP Report 118 (2007) recommends two approaches for estimating BRT ridership: The first
approach is to use either a four-step travel demand model or an incremental logit model. The
four-stop model approach is appropriate for a system (or corridor) over long time horizons and
with large-scale investments. The information needed includes estimates of current and future
employment, surveys, land uses, and flows in the corridors. However, TCRP Report 118 (2007)
advises that whenever possible, the incremental logit modeling approach should be used instead
of running a full-scale travel demand model, since it only requires describing the system
components that are anticipated to change and the individual passenger data for the influence
area of the route or corridor under study.
The incremental logit methodology estimates the changes in modal share based on the changes in
level of service. The predicted changes in level of service are applied to the base OD matrix to
predict future changes in mode choice. The future mode share is a function of the existing mode
share and the change in utility for the mode of interest compared with changes in utility for all
modes being analyzed. The utility functions usually include out-of-vehicle time, in-vehicle time,
transfers, and fare. The formulation of the incremental logit model is as follows:
P.
Pi P
=kv
A.U,
,
e
(2-4)
AUi
where:Pi is the baseline probability of using mode i
P'i is the revised probability of using mode i
AUi is the change in utility for mode i
k is the number of travel modes available
The second approach, which is also suitable to conventional limited-stop services overlapping
with existing local services, is to apply service elasticities 3 . The application of the elasticity
methodology is more appropriate for short time horizons, small-scale investments, and where the
new service is overlaid on an existing service. The procedure consists of applying the various
elasticities to service changes (i.e. fares elasticity to fare changes, travel time elasticity to travel
time changes, and frequency elasticities to frequency changes).
Service elasticities have been estimated from a variety of sources; however, these values can
vary significantly across countries, regions, and cities. For this reason is recommended that each
city develop its own values if possible. Table 2-1 presents a summary of the different services
elasticities found in the literature.
3 Defined as the % change in ridership over the %change in level of service (i.e. travel time, frequency)
Table 2-1 Typical service elasticities
Item
Application
Range
Bus Miles
Bus Frequency
Travel Time
New routes or routes
complementing existing
services
Service
expansion
Greater Frequency
of existing route
-0.3 to -0.5
0.6 to 1.0
0.3 to 0.5
Source: TCRP Report 99 and TCRP Report 118
TCRP Web Document 12 (2000) summarizes how changes in other service attributes (including
span of service, out-of-vehicle times, reliability, and new express services) affect ridership.
However, the document does not provide elasticities but instead presents evidence from different
cases of how changes in these variables affected ridership.
There is consensus in the literature that branding is a major element that can attract ridership to
BRT services in addition to service improvements. The branding element is so important that
some critics of limited-stop service, such as the Metro Rapid in Los Angeles, claim that those
services are "a triumph of marketing over substance"4 .
Different sources including Henke (2006), Baltes (2003), and Tann et al (2009) state that
between 20% and 33% of ridership gains or increases in overall customer satisfaction with BRT
service cannot be explained by traditional factors such as travel time, frequency, reliability, or
capacity improvements but rather result from improvement in image and perception of the new
service (branding).
Despite the fact that different documents acknowledge the effect of branding on ridership, the
TCRP Report 118 (2007) is the only document that suggests a method for estimating this effect.
The method can be used either when predicting the new demand with a four-step travel demand
model or with an elasticity approach. When using a four-step travel demand model, the report
recommends adding a travel time bias constant to the BRT alternative equivalent to up to 10
4
http://greatergreaterwashington.org/post.cgi?id=4638
minutes of in-vehicle time. When an elasticity method is used, the report suggests that BRT
could attract up to 25% more riders than that obtained simply by applying elasticities.
Table 2-2, taken from the TCRP Report 118, shows how the different BRT components affect
the branding element in the ridership estimation process.
Table 2-2 Additional ridership impacts of selected BRT components
COMPONENT
Running Ways (not additive)
Grade-separated busway (special right-of way)
All-day
arterial
bus
lanes
10%
busway
(specially
20%
20%
15%
At-grade busway (special)
Median
PERCENTAGE
delineated)
Peak-hour bus lanes
Mixed traffic
Stations (additive)
Conventional shelter
Unique/attractively designed shelter
llumination
Telephones/security phones
Climate-controlled waiting area
Passenger amenities
Passenger services
Vehicles (additive)
Conventional vehicles
Uniquely designed vehicles (external)
Air conditioning
Wide multi-door configuration
Level boarding (low-floor or high platform)
Service Patterns (additive)
All-day service span
High-frequency service (10 min or less)
Clear, simple, service pattern
Off-vehicle fare collection
ITS Application (selective additive)
Passenger information at stops
Passenger information on vehicles
BRT Brandina (additive)
Vehicles and stations
5%
0%
0%
15%
0%
2%
2%
3%
3%
3%
2%
15%
0%
5%
0%
5%
5%
15%
4%
4%
4%
3%
10%
7%
3%
10%
Source: TCRP Report 118
The proposed methodology can be divided into four steps. The first step is to select and add the
BRT points for any components included in the proposed new BRT service to obtain the
subtotal. The second step is to add the synergy points; if the subtotal points are greater than 60,
the synergy points (15 points) are added, and if not, no points are added. The third is to add the
subtotal and the synergy points to obtain the total points. When an incremental logit approach is
used, the fourth step is to multiply the total points by 10 and the result is the bias constant that
should be added to the BRT alternative in the discrete choice model. When an elasticity approach
is used, the fourth step is to multiply the total points by 0.25 and that number is the percentage
increase in ridership (with respect to the baseline riders that choose the BRT service over the
local service) due to the branding effect.
2.3
Passenger Service Choice
Previous research on how passengers choose between local and limited-stop services is sparse.
TCRP Report 118 (2007) suggests that when a BRT operates as a limited-stop service
configuration overlapping with a local service, the ridership allocation can be based on
judgment, equal division between the services, or based on patterns of boarding and alighting
and relative travel times. The document recommends that the split should be based upon origin to
destination and boarding/alighting patterns, and/or market research but does not present a
methodology.
TCRP Report 118 provides the following set of equations that establish possible demand
allocations based on relative running times between the BRT limited-stop and the local service.
+
PLim ited
(
2
-5)R
BRT
-
Limited
~Limited
1+
tBRT
(2-6)
±tRT(2)
-1
tBRT
e I + e tBRT
PLimited
(2-7)
e
Limited
--
P~mtd
e
1-t_T
tBR T
1+e
where:PLimited
tBRT
(2-8)
is the share of riders assigned to the limited-stop service
is the BRT (limited-stop) service running time relative to local service running time
The validation of the Schwarcz model with the Ashland and Cicero limited-stop services in
Chicago showed that similar frequencies on the local and the limited-stop service do not
necessarily lead to similar demand split. In these cases given equal frequencies the demand split
ranged between only 33% and 42% riders for the limited-stop service. The reasons for this
include the lack of awareness and understanding of the limited-stop service, the small percentage
of riders that always take the limited-stop service, and the large percentage of riders that take
whichever bus comes first 5 .
2.4
Running Times
This section focuses on the estimation of running times for BRT services since this is more
complex than the conventional limited-stop service case.
TCRP Reports 26 (1997), 90 (2003), 100 (2003), 118 (2007), and the NBRTI document (2008)
illustrate how each of the BRT components affects running times. The travel time savings of
BRT systems are a result of the combination of all the components; thus, it can be hard to isolate
the effect of each of them since the results presented in the reports are usually empirical and
based on the experience in different cities rather than on a modeling approach. In addition, the
effectiveness of different components depends on the current split of the running time into
movement, dwell, traffic, and traffic signal times. For example, a corridor with a small number
of signals and where the bus delays are mainly affected by congestion will benefit more from a
completely segregated right-of-way than from the implementation of Transit Signal Priority.
In the next sub-sections the potential travel time savings and speed increases for the different
BRT components are discussed.
2.4.1
Right-of-way
The level of right-of-way segregation of the bus system is one of the more important variables
affecting speed although speed is also a function of the stop spacing, dwell times, and traffic
5Established by a survey carried out by the author during March 2010
signals. Table 2-3 through Table 2-5 summarize bus speeds as a function of stop spacing and
dwell times for busways and HOV lanes, for dedicated arterial lanes, and for general purpose
traffic lanes respectively.
Table 2-3 Bus Speeds on Busways and Exclusive Freeway HOV Lanes
Average Stop
Spacing (miles)
0.5
1.0
1.5
2.0
2.5
Averae Dwell Time
15
30
21 mph
26 mph
30 mph
34 mph
35 mph
38 mph
37 mph
41 mph
39 mph
42 mph
er Sto s secs
45
60
16 mph
18 mph
24 mph
27 mph
29 mph
32 mph
32 mph
35 mph
35 mph
37 mph
Note: Assumes 50 mph top running speed in bus lane
Source: Characteristics of Bus Rapid Transit for Decision-Making
Table 2-4 Bus Speeds on Dedicated Arterials
Average Stop
Spacing (miles)
0.1
0.2
0.3
0.5
Average Dwell Time
10
20
7mph
9mph
13 mph
16 mph
15 mph
18 mph
22 mph
25 mph
per Stos
30
6mph
11 mph
13 mph
20 mph
(secs)
40
5mph
10 mph
11 mph
18 mph
Note: Does not include the effect of traffic or signal delays. TCRP
Report 100 establishes correction factors for accounting those
Source: Characteristics of Bus Rapid Transit for Decision-Making
Table 2-5 Bus Speeds in General Purpose Traffic Lanes
Average Stop
Spacing (miles)
0.1
0.2
0.3
Average Dwell Time
10
20
5mph
6mph
8 mph
9 mph
9 mph
10 mph
0.5
11 mph
10 mph
per Stops
30
5mph
7 mph
8 mph
(secs)
40
4mph
6 mph
7 mph
10 mph
9 mph
Source: Characteristics of Bus Rapid Transit for Decision-Making
2.4.2
Transit Signal Priority (TSP)
There is no agreement in the literature on how much travel time savings can be attributed to TSP.
This is in part because of the different priority treatments available (including passive, active,
real-time, and preemption) and also because transit agencies rarely provide comparable statistics.
For example, reductions in traffic delays or travel times are often cited without providing further
detail such as whether these reductions represent a saving of 2 seconds per intersection or 45
seconds per intersection, or whether the travel time savings are estimated for an average
passenger trip or for the entire route.
According to TCRP Report 118 (2007), travel time savings associated with TSP in North
America and Europe have ranged from 2% to 18% with a typical reduction of 8%to 12%. The
Evaluation Report for the City of Los Angeles (2003) established that for the case of Ventura and
Wilshire/Whittier Corridors in Los Angeles, where 20% of the base running time was spent at
traffic signals, the implementation of TSP reduced the traffic signal time by 35%.
Furth 6 acknowledges the difficulty and lack of literature regarding the prediction of travel time
reductions as a result of the implementation of a TSP strategy. Furth suggests the following to
assess the percentage reduction in time spent at traffic lights:
0
10 to 20% traffic delay reduction for the typical timid priority applied in the US
*
PLUS 20 to 30% further traffic delay reduction from having a queue jump lane
*
PLUS 20% further traffic delay reduction from using aggressive priority tactics
2.4.3
Queue jumps/Bypass lanes
BRT vehicles can bypass traffic queues at intersections through the application of a "queue
jump" or "bypass lane". TCRP Report 118 estimates that travel time reductions as a result of the
application of these elements are between 7-27 seconds per bus intersection with the highest
savings during peak periods.
6 Interview
by author with Professor Peter Furth. Department of Civil and Environmental Engineering, Northeastern
University, 2010
2.4.4
Vehicle design and fare collection
The vehicle size and floor level play important roles in dwell times and can have a major impact
on the running times depending on the number of stops, and the total ridership on a route. Wright
(2007) stresses that reductions in dwell times are particularly important in high capacity BRT
systems where the most efficient vehicle design can reduce passenger service times to as little as
0.3 seconds per passenger (Transmilenio in Bogota).
Table 2-6 and Table 2-7 show the service time per passenger on a vehicle with a single door and
multiple doors respectively.
Table 2-6 Passenger service times with single-channel passenger movement
Passenger Service time (s/p)
Observed Range Suggested Default
Situation
BOARDING
2.5
2.25 to 2.75
Pre-payment*
3.5
3.4 to 3.6
Single ticket or token
4.0
3.6 to 4.3
Exact change
4.2
4.2
Swipe or dip card
3.5
3.0 to 3.7
Smart card
ALIGHTING
3.3
2.6 to 3.7
Front door
2.1
1.4 to 2.7
Reardoor
Note: * includes no fare, bus pass, free, and pay-on-exit.
Note: Add 0.5 s/p to boarding times when standees are present
and subtract 0.5 s/p from boarding and alighting times for lowfloor buses
Source: TCRP Report 100
Table 2-7 Passenger service times with multiple-channel passenger movement
Default Passenger Service Time (s/p)
Available
Rear Alighting
Front Alighting
Door Channels Boarding*
2.1
3.3
2.5
1
1.2
1.8
1.5
2
0.9
1.5
1.1
3
4
0.9
1.1
0.7
6
0.6
0.7
0.5
Note: * Assumes no on-board fare payment required
Note: Increase boarding times by 20% when standees are present. For lowfloor buses, reduced boarding times by 20%, front alighting by 15%, and
rear alighting times by 25%
Source: TCRP Report 100
2.4.5
Stop spacing
Stop spacing is the only major design component that a conventional limited-stop service has in
common with BRT service. Table 2-8 provides estimates of bus running speeds as a function of
stop spacing (without including the effect of dwell time) which are consistent with the average
bus speed presented in Table 2-4 for dedicated arterial bus lanes. It is important to recognize that
a reduction in stops might result in more passengers at each stop which will offset some of the
benefits of fewer stops.
Table 2-8 Base Running Speeds for Dedicated Arterial Bus Lanes
Stops per mile
Dwell Time
2
4
5
0s
2.06 min/mi
2.61 min/mi
2.94 min/mi
6
1
3.3 min/mi
1
7
8
10
3.72 min/mi
4.2 min/mi
5.34 min/mi
Note: Interpolated from a Table with a range between 10-60 s dwell times. The original table does not provide
numbers for 0 s dwell times but suggests that these values can be interpolated.
Note: The provided values assume no signal or traffic delays.
Note: For Central Business District (CBD): Add 1.2. CBD + Right Turns: Add 2.0. CBD + Right Turns + Lanes
blocked by Traffic: Add 3.0.
Note: For Arterial Roads with no traffic: Add 0.5-1.0. For Arterials with mixed traffic: Add: 1.0
Source: TCRP Report 100
2.5
Experiences in Cities with Conventional Limited-Stop and BRT Service
This section describes two case studies of cities with significant experience in the operation with
limited-stop and BRT services: Transmilenio in Bogota (Colombia) and Los Angeles County
Metropolitan Transit Authority.
2.5.1
Transmilenio
Bogota implemented the Transmilenio BRT system in December 2000. Transmilenio currently
consists of 84 km of interconnected BRT lines, a fleet of 1,074 articulated buses and 5 biarticulated buses, and 114 stations. An integrated feeder network of 515 km and 438
conventional buses complements the BRT network providing access from surrounding
neighborhoods. Figure 2-2 shows the current system.
IMAPA GENEA
Figure 2-2 Transmilenio System
Transmilenio was conceived as a long term expansion plan consisting of seven Phases. At the
moment Phases I and II7 have been implemented and Phase III is under construction. However,
the implementation plans for the remaining phases are unclear due to the possible
implementation of a rail-based transit system. The plan for Phase III, which had contemplated
the implementation of 3 new corridors, has been modified. The new plan includes the original
Avenida 26 and Avenida 10 corridors, but the Avenida 7, the third corridor, will possibly include
just a segment of the original design, no overtaking lane at the stations, and some priority lanes
(different from the fully segregated lanes typical of Transmilenio).
The system is recognized world-wide for many reasons including its high capacity, high level of
service, and low cost. During the morning peak period the system carries up to 43,000
I included the Avenida Caracas, Autopista Norte, and Calle 80 corridors. Phase II included the Avenida
NQS, Avenida de las Americas, and Avenida Suba corridors
7 Phase
pas/hour/dir (Steer Davies Gleave, 2007) and serves a daily demand of 1.5 million trips that
represent 19% of all journeys made in Bogota. Surveys showed that the system was perceived by
the great majority of Bogotanos to be a better option than the traditional public transportation
services. Additionally, at least 10% of Transmilenio riders own a private automobile. After the
implementation of the first two corridors (Avenida Caracas and Calle 80) bus speeds increased
from 12 to 26 km/h in these corridors and the number of accidents in the Transmilenio corridors
was also reduced significantly from 1060 to 220 per year, between 1999 and 2001 (Yepes, 2003).
Key elements which allow the system to carry such high passenger volumes with high levels of
service include:
High-capacity buses. Transmilenio uses articulated high-floor buses with 160-passenger
capacity and multiple wide doors, combined with high platforms to provide level boarding. In
August 2009, the system started operating 5 bi-articulated buses with a 260-passenger capacity.
Figure 2-3 shows the standard and bi-articulated Transmilenio buses.
Figure 2-3 Articulated and Bi-Articulated Transmilenio Buses
Exclusive lanes. All the corridors allow overtaking. In some cases the system has two exclusives
lanes in each direction, and in others the system has one exclusive lane between stations and two
lanes at each station to allow express buses to overtake local buses. Figure 2-4 shows the typical
configurations.
Figure 2-4 Transmilenio Right-of-Way
Multiple stop bays at stations. The system use stations instead of stops with each station having
between 1 and 3 platforms. Each platform can serve up to 80 buses per hour and has room to
serve two buses in the same direction at the same time although typically one bus uses the
platform at a time while the second waits for the platform to clear before serving passengers
(without blocking the overtaking lane). Figure 2-5 shows a typical 3-platform station.
Figure 2-5 Transmilenio station
Enhanced boarding, There are different elements that together allow the system to have dwell
times similar to metro systems. These elements are the use of contact-less smart cards that
passengers must validate when entering a station, 3 wide doors on each articulated bus, and level
boarding. Figure 2-6 shows the boarding and ticket validation processes at stations.
Figure 2-6 Boarding and ticket validation processes
Express and local services. To accommodate the high level of ridership, Transmilenio offers
two main types of services: express and local. Without this it would be impossible for the
stations along the busiest corridors to serve the large number of buses (more than 300) operating
during the morning peak hour. It also increases the quality of service since the express services
are faster than local services. The express services are particularly popular with customers
making longer trips since significant travel time savings can be realized. At some stations
customers have up to 10 different routes (local and express) available from which to choose.
The average speed of the system is 22 km/h with highest speeds in the Norte Quito Sur (NQS)
corridor and the slowest speeds in the Eje Ambiental corridor. The average speed of local
services is 19 km/h and express services is 23 km/h. In the Avenida Caracas corridor, local buses
spend 56% of the time moving, 23% at stations serving demand, and 21% at stop lights; while
express buses spend 62% of the time moving, 14% at stations, and 24% at stop lights (Steer
Davies Gleave, 2007).
The system actually uses a third type of services during weekday peak periods called superexpress services which operate with fewer stops than express services.
To deal with the difficulty that new and occasional passengers have identifying the routes that
serve their origin and destination, Transmilenio uses a complex nomenclatures representing the
user's trip in an array of colors, numbers, and letters. This nomenclature is not easily understood
by riders and it led to a city-wide protest from passengers when a route redesign was performed
after the implementation of Phase II. An important lesson from that experience is that the
appropriate design of local and limited-stop services should focus not only on reducing travel
times but also on ease of understanding for the customers.
2.5.2
Los Angeles County Metropolitan Transportation Authority (LACMTA):
LACMTA runs bus and heavy and light rail systems. The bus system is comprised of
conventional (Metro Local), BRT-lite (Metro Rapid), and BRT (Orange Line) services as
described bellow:
Metro Local. Metro Local is the conventional bus service providing local, limited-stop
(sometimes branded as Metro Limited), express (sometimes branded as metro express) and
shuttle services. Buses are distinguished by their orange color (See Figure 2-7) although some
old buses remain white with an orange stripe.
Figure 2-7 Metro Local bus
Metro Local buses have numbers indicating the type of service they offer:
*
1-99: Local bus services to/from downtown Los Angeles and other areas
*
100-299: East-west and north-south service, not necessarily serving downtown LA
*
300-399: Metro Limited. Limited-stop services on local routes which make fewer stops
and generally operate only during peak periods. Many have been replaced by Metro
Rapid services
e
400-500: Metro Express. These services have a different fare structure and usually run
express for a portion of the route, then run either local or limited-stop in other areas. The
main difference from the Metro Limited is that express services operate on freeways or
transitways on a portion of their trip
*
600-699: Shuttle and special event services
Surveys of customers' perception of different LA transit modes carried out by Tann et al (2009)
show that the Metro Local service has a severe image problem, and is generally regarded as a
"lower-class" mode. Riding the bus carries a "shame factor" and most choice riders would not
consider using it. Moreover, the fact that regular buses run in mixed traffic, and thus are prone to
unreliability and travel delays due to congestion, is seen by riders as a major drawback compared
with other transit modes.
Metro Rapid. This is the BRT-lite service of Los Angeles operating 26 routes across a network
of 450 miles (See Figure 2-8).
Metr Rd
SyseMa
WiO
R~pid
rmlin
p
ACMTA
l0130
urn3
7
Fg_
1
MCA
SNTA
rmw
-
Elaborated by: Robert J. McConnell
Figure 2-8 Metro Rapid System
The main characteristics of the service are the limited-stops, spaced about % mile apart at major
intersections and transfer points; the signal priority system, which grants priority to buses by
extending the green phase or shortening the red phase of traffic signals; special low-floor buses
differentiated from the rest of the fleet with a red-paint scheme; and special stop stations
allocated at the far-side corners of intersections, beyond traffic lights, while local stops are on the
near corner. These stop stations provide canopies, information, lighting, and real-time passenger
information (electronic displays showing waiting passengers when the next bus is arriving).
Figure 2-9 shows a Metro Rapid bus and stop.
Figure 2-9 Metro Rapid bus and station
The Metro Rapid program was launched in June 2000 and was implemented originally on only
two lines. Since then, the Metro Rapid network has grown, replacing old limited-stop services
(Metro Limited) further increasing their stop spacing. TCRP Report 90 (2003) reported speeds of
the services are between 14 and 19 mph and the reported travel time saving are between 23% and
28% compared with the old system for the Ventura and Wilshire-Whittier Boulevards. The same
corridors generated 26% and 33% increases in ridership. From those increases, between 16% and
20% could be explained by increases in frequency and speeds and the remaining 10% and 13%
due to other changes (including branding).
There have been two complaints from the customers about the system. The first is the separation
of stops between local and the rapid service. This has lead to a dangerous phenomenon called the
"Rapid Bus Shuffle", where waiting passengers at either stop runs to the other stop if a bus
arrives. The second complaint was that many of the resources for Metro Rapid came from cuts
in local service so riders not close to a Metro Rapid stop must wait longer than before or walk a
longer distance to a Metro Rapid stop. For that reason the Bus Riders Union (BRU) filed a legal
complaint and obtained a consent decree, with the LACMTA, establishing that the resources for
Metro Rapid cannot come from cuts in local services of more than 33% (Consent Decree, Load
Factor Service Plan, 2003).
According to the survey of customers' perception of different LA transit modes carried out by
Tall et al (2009), Metro Rapid is viewed by riders as express service which is part of the regular
bus network. Despite the fact the users that have ridden it found it "reliable, quick, and with good
real-time information" the system was not seen as a different transit mode. Therefore the Metro
Rapid service is susceptible to the same negative perceptions associated with regular bus service.
Metro Liner (Orange Line). The Orange Line is Los Angeles' full BRT system and one of the
first full BRT systems in the US. The Orange Line began operations in October 2005 and
consists off a 14-mile dedicated line that runs east-west through the San Fernando Valley (See
Figure 2-10). The LACMTA identifies the Orange Line as a Metro Liner mode which is neither
part of the metro rail system nor part of the metro bus system. However, the Metro Liner mode is
meant to mimic the Metro Rail lines and in the maps it appears as another Metro Rail line. The
Orange Line runs between Warner Center and the North Hollywood Metro Red Line subway
station in the San Fernando Valley. Recently (December 2009), the Silver Line (another Metro
Liner system) started operating on a 26-mile route between El Monte Station and the Artesia
Transit Center near Carson.
Canoga
PaIbo
0ec.,e
De~~~,
Woodley
SooTmaVn
0
Sepulveda
N ys
m
0
0rI
Warner
Center[
ValeyCoIegeo
bNorth HoHywood
Canyon
Figure 2-10 Los Angeles Orange Line
The main characteristics of the service are high-capacity low-floor articulated vehicles with three
doors, differentiated from the rest of the fleet by a special silver paint scheme; rail-like stations;
off-vehicle fare payment, that combined with the vehicle design, allows level boarding through
all doors; and transit signal priority. Figure 2-11 shows an Orange Line bus.
Figure 2-11 Los Angeles Orange Line bus
A review of the performance performed by Vicent and Callahan (2007) of the Orange Line after
one year of operations found that it had an average speed of around 17 mph in the peak periods
(a similar speed to the Metro Rapid line running parallel on the Ventura Line). The LACMTA
had projected the line to have between 5,000 and 7,500 average.weekday boardings for the first
year of operations and 22,000 average boardings by 2020; however, by May 2006, the system
had already achieved its 2020 goal. The Orange Line grew from 16,360 weekday trips in
November 2005 to 20,619 weekday trips in November 2006 while the total ridership of the Gold
Line, a 13.7-mile light rail service opened in 2003 and with a planned ridership of 30,000
boardings, grew from 16,910 weekday trips to 17,638 trips in the same period.
According to the survey of customers' perception of different LA transit modes carried out by
Tann et al (2009), the Orange Line service is viewed as something between the regular bus and
rail. The document points how some customers use terms like "train-bus" and "bourgeois bus" to
refer to the system. These terms suggest that the system "had succeeded in conveying the image
of a premium rapid transit mode on a par with rail-based services."
Tann et al (2009) also illustrate (See Figure 2-12) the capital cost vs. customer perception of
different bus and rail services in LA. It is important to highlight from the figure that a BRT-
lite limited-stop service and the Full BRT services both have a good image comparable to more
costly rail systems. For example, the Metro Rapid system required a minimum investment and
customers perceive it on par with the Blue Line (LRT).
4
4.
0
Red Line HRTjg
Orange Line BRT
..
GodLne LRT
[L Metro Rapid 'BRT Lite
1Blue Line LRT
T er 3
Tier 1
1
Local buLs
0
50
100
150
200
250
300
350
Capital Cost per Mile ($M, 2005 dollars)
Figure 2-12 Overall customer rating of different transit modes in LA vs. Capital Cost
2.6
Summary
To conclude, this chapter has presented a review of the methodology and model developed by
Schwarcz to evaluate limited-stop services overlapping with local services, prior research on the
topic from academia and industry, and a description of two cities' experiences with these
services. This research will be used to develop an improved model with the capability to predict
ridership increases, assign demand with a probabilistic approach, and model both limited-stop
and BRT services. The model will be applied to case studies in Chicago, and some general
recommendations will be generated from the modeling process, empirical Chicago results, and
other cities' experiences for the design and evaluation of limited-stop and BRT services
overlapping with local services.
3
PREDICTING RIDERSHIP CHANGES
The preceding chapter reviewed the model developed by Schwarcz to evaluate limited-stop bus
services that overlap with local services, pointed out some of its limitations, and reviewed prior
research related to forecasting ridership changes associated with the implementation of such
services. This chapter provides a methodology, built upon prior research and the experience from
the Chicago Transit Authority (CTA) with its X-Routes (limited-stop services), to predict
changes in ridership when a limited-stop (or BRT) service is introduced overlapping with an
existing local service. The proposed methodology considers the ridership changes due to changes
in passengers travel time and the effect of branding (in BRT cases).
Travel time elasticities are the values often used to predict ridership impacts of changes in
passenger travel time. This chapter presents a method for obtaining these values by measuring
ridership changes and passenger travel time changes where limited-stop services have been
implemented. After the elasticities are estimated, they can be used to predict ridership by
combining their values with the modeled changes in passenger travel time.
This chapter is divided into three sections. The first section explains in detail the proposed
method for predicting ridership changes due to the implementation of limited-stop (or BRT)
services with local service. The second section shows how travel time elasticities were estimated
in a case study in Chicago (Route X9 in the Ashland Corridor). This section describes the
changes implemented in the corridor, how those changes affected ridership and passenger travel
time, and computes travel time elasticities8 . The third section explains the procedure to account
for ridership increases due to the effect of branding. This effect is generally observed only when
BRT services are implemented.
8 The elasticities found in this section can be safely used for predicting ridership changes in corridors with similar
characteristics in Chicago; however, for predicting ridership changes in other US cities it is strongly recommended
estimate city-specific elasticities and use these values in forecasting.
3.1
Proposed Methodology
The proposed methodology has two steps to predict the change in ridership when a limited-stop
or BRT service is introduced. The first step forecasts the ridership changes due to changes in
passenger travel time (including access, egress, waiting, and in-vehicle time) based on the travel
time elasticity. The idea is to account for the effect of changes in service speeds, frequencies, and
stop spacing in one step rather than using separate elasticities for frequency and in-vehicle travel
time as suggested in the literature (TCPP Report 95, 2004). The second step forecasts the
ridership increases due to the branding effect described in TCRP Report 118 (2007) and laid out
in Chapter 2 (section 2.2) of this thesis. This factor captures the extra ridership gains that are
intrinsic to BRT systems with a distinct brand that cannot be explained strictly as a result of
service improvements.
In order to estimate the elasticities used in the first step, a data set with information prior to and
after the implementation of a limited-stop service is needed. An ideal data set would include, for
different routes, the total travel time (split into access, egress, waiting, and in-vehicle time) for
each passenger before and after the implementation of the limited-stop service as well as trip
characteristics, such as trip purpose and time of day, and passenger characteristics such as age,
gender, car availability, willingness to walk, etc. With such a data set, it would be possible to
compute the elasticities for different trip lengths, trip purposes, times of day, etc. and to test if
there are significant differences between them.
Unfortunately the available data is limited, and collecting a data set with the aforementioned
characteristics would be burdensome. The data used on this thesis includes only the demand at
the stop level, headways, headway coefficients of variation, and service running times, before
and after the implementation of various limited-stop bus services in Chicago, including a full set
of data for Route 9 and X9, in the Ashland corridor, and a set of data for the AM period for
Route 54 and X54, in the Cicero corridor.
With the available information, travel time elasticities can be estimated for different trip lengths
and times of day. In cities with no experience with limited-stop services, values can be taken
from corridors with similar characteristics in other cities. Although, it is preferable that each city
estimates its own values. The hypothesis is that shorter trips have smaller elasticities than longer
trips. The proposed formulation is presented in Equation (3-1).
TravelTimeElasticity =%
3.2
Change in Ridership
% _Change _ in _ Travel _Time
(3-1)
Measuring Travel Time Elasticities in the Ashland Corridor in Chicago
The following sub-sections describe the service changes and estimate travel time elasticities for a
case study in the Ashland Corridor in Chicago where limited-stop service was introduced. These
values will be used later in case studies (Chapter 6) to forecast ridership changes due to the
implementation of conventional limited-stop and BRT services in Chicago.
3.2.1
Service changes on the corridor
In summer 2006 the Chicago Transit Authority (CTA) implemented Route X9 overlapping with
Route 9 (the old local service). Route 9 is a 20-mile route running from Irving Park, south along
Ashland to 95th Street, with some buses continuing on to 103rd Street/Vicennes. This local route
has 136 stops for the service running to 95th Street with an additional ten stops for the service
running to
10 3 rd
Street. The new Route X9 runs on the same arterial to
9 5 rd
street, but with only
39 stops. Both services connect with six rail lines (Orange, Pink, Blue, Green and Brown line) as
shown in Figure 3-1.
Most Route X9 resources (platform hours) were taken from Route 9 with a few extra trips added.
The combined peak period platform hours increased from 85.3 in fall 2005 to 91.0 in fall 2006
(Sweat, 2007). This means that the new combined headway of the local and limited-stop service
was the same as the prior headway on the local route. Figure 3-2 shows the change in combined
headway in the Ashland corridor between Fall 2005 and Fall 2007. As shown, most of the new
resources were added during the midday and evening off-peak periods and in general brought
some longer scheduled headways during these periods into line with the rest of the day.
SB Headways (Fall 05 and 07)
NB Headways (Fall 05 and 07)
o~V c'j
-T- -, t
T0~
0C1
Time of day
(2007) -9
-9/X9
Time of day
(2005)
(2007) -9
in9/X9
(2005)
Figure 3-2 Combined headways before and after the implementation of Route X9
For the peak periods there was only a small increase in frequency on the combined routes. Table
3-1 shows the changes in headways for those periods. Most service increases were in the AM
period in the SB direction where the headway was reduced from 8 minutes in fall 2005 to 5
minutes in fall 2007.
Table 3-1 Headways on Route 9 and X9
PM Peak
AM Peak
S
9
P
Both
BSB
8.5
9
12.0
X9
50
5.0
Both
8.5
1
SB
-,12.0
8.0
X9
80
2006
2005
2007
2006
2005
2007
SB
EEE
1EE
12.
Based on these headways, Table 3-2 shows the frequency split (the percentage of total bus trips
that are provided on the limited-stop and the local service) between Routes 9 and X9. The only
imbalance in the frequencies is in the AM peak in the SB direction where the frequency split is
59% for the local service.
Table 3-2 Frequency split between Routes 9 and X9
AM Peak
2006
9
X9
3.2.2
NB
53%
47%
SB
59%
41%
PM Peak
2006
2007
NB
53%
47%
SB
59%
41%
9
X9
NB
52%
48%
SB
52%
48%
2007
NB
50%
50%
SB
52%
48%
Ridership Changes
Ridership increased steadily from several months after the implementation of the limited-stop
service with the exception of only one month9 . From fall 2005 to fall 2007 ridership in the
Ashland corridor increased from 32,078 daily trips to 35,825 daily trips, an increase of 11.7%
compared with an increase of 2% on the full CTA bus system over the same period. Figure 3-3
shows ridership from June 2005 (a year before the introduction of Route X9) to December 2007
(18 months after its introduction) with the arrow indicating the start of Route X9 (June 2006).
40000
36000
m 2005
32000
i2006
28000
2007
24000
20000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Figure 3-3 Average Daily Boardings Route 9/X9
9February 2006 showed a decrease in ridership vs. February 2007 that can be explained by bad weather that month.
See Guo et al. Weather Impact on Transit Ridership in Chicago (2007)
Figure 3-4 shows in more detail the ridership increases during the AM and PM peak periods
between fall 2005 (prior to the implementation of Route X9), 2006 and 2007. It is noteworthy
that from fall 2006 to 2007 most of the ridership increases were seen on Route X9 rather than on
Route 9. Minor ridership increases were seen between 2005 and 2006 (a few months after the
implementation of Route X9); however, by fall 2007 ridership had increased in both directions
(NB and SB) and both periods. It increased by 7.3 % in the AM peak from 6,664 trips in fall
2005 to 7,148 trips in fall 2007; and by 12.1% in the PM peak from 7,730 trips in fall 2005 to
8,864 trips in fall 2007.
AM Period NB
AM Period SB
5000
50004000
-
:E 3000
-
V0 2000
-
4000
0.
0.
* X9
.9
E 3000
2000
.
1000-
1000
0-
0
Fall 2005
Fall 2006
Fall 2007
Fall 2005
Year
Fall 2007
Year
PM Period NB
PM Period SB
50004000
Fall 2006
5000
4000
0.
V
3000 -
* X9
2000
.9
-
1000-
a)
3000-
* X9
2000-
.9
1000-
01
0Fall 2005
Fall 2006
Year
Fall 2007
Fall 2005
Fall 2006
Fall 2007
Year
Figure 3-4 Route 9/X9 ridership
The ridership split between the local and the limited-stop services is shown in Table 3-3. Despite
the fact that the frequency split for most of the periods and directions is close to 50/50 (See Table
3-2), the ridership split is not close to 50/50: more riders take the local service than the limited-
stop service.
Table 3-3 Route 9/X9 ridership split
AM Peak
2006
9
NB
60%
SB
59%
X9
40%
41%
PM Peak
2006
2007
NB
57
43%
SB
59%
9
NB
61%
41%
X9
39%
SB
2007
63%
NB
57%
SB
67%
37%
43%
37%
The average trip length can be estimated based on the boardings and alightings at each stop and
is shown in Table 3-4 before and after the implementation of Route X9. As expected, the local
service is used more for short trips and the limited-stop service is used more for longer trips.
However, the average trip lengths are still relatively short given the 20-mile route length.
Table 3-4 Route 9/X9 average trip lengths (miles)
2005
NB
X9
AM Peak
2006
2007
SBj NBl SB
3.5
3.0
NB
3.4
2005
SB
2.8
NB
X9
PM Peak
2006
SB 1NB
2.9
2007
SB
3.3
NB
2.7
25
3.2
Computing the travel time elasticities on Route 9/X9 for different trip lengths (as proposed in
section 3.1) requires assessing how the ridership increases occurred by trip length and what the
relative changes in travel time were for the same trip lengths.
To estimate the ridership increases for different trip lengths, Origin-Destination (OD) matrices
were estimated, before and after the implementation of Route X9. The method used to build the
OD matrices, without using an OD survey to generate the seed matrix, is the one proposed by
Navick and Furth (1994). The information required for this estimation includes the stop level
boardings and alightings for the period and the distance matrix which is used as the seed matrix.
Figure 3-5 shows the trip length histograms resulting from the estimated OD matrices. Even
though there was an overall increase in ridership, as shown in Figure 3-3, there was an initial
reduction in the number of short trips, which was mostly recovered 18 months after the
implementation of Route X9. Most of the ridership gains occurred for longer trips.
AMSB
AM NB
1400
1400
1200
1200
1000
1000
:E
800
o
600
82005
2006
U 2007
800
92005
m 2006
600
U 2007
400
400
200
200
0
0
0
2
4
6
8
10
12
0
Trip Length (Miles)
2
4
6
8
10
12
Trip Length (Miles)
i
PM NB
PM SB
1400
1400
1200
1200
1000
1000
800
U 2005
800
U 2005
600
U 2007
a2006
600
U 2007
92006
400
400
200
200
0
0
0
2
4
6
8
10
Trip Length
0
2
4
6
8
10
12
Trip Length (Miles)
Figure 3-5 Route 9/X9 trip length histograms
Figure 3-6 shows the percentage change in ridership for different trip lengths, directions, and
time periods. As expected, longer trips grew more than shorter trips; however, there is variation
in the rate of growth by direction and time period. This variation is due to differences in travel
time, trip purpose, and passenger characteristics.
PM Peak
AM Peak
20%
20%
15%
15%
10%
10%
5%
SB
*NB
5%'SB
0%
0%
2
3
More
0
1
2
3
-5%-5%
Trip Length (Miles)
More
than 4
Trip Length (Miles)
Figure 3-6 Route 9/X9 ridership change by trip length, trip period and direction
3.2.3
Changes in Passenger Travel Time
In order to assess the changes in passenger travel time, it is necessary to compute the passenger
travel times before and after the implementation of Route X9. The method for estimating the
travel times is that proposed by Schwarcz and described in section 2.1.
The model requires estimating the changes in access, egress, waiting, and in-vehicle times for all
passengers before and after the implementation of the limited-stop service, assuming that each
passenger chooses between waiting for the local, waiting for the limited-stop, or taking the first
bus that comes based on the alternative that minimizes her weighted travel time.
To estimate the access times, the waiting times, and the in-vehicle times, the model requires as
inputs: headways, headway coefficient of variation, and speeds before and after the
implementation of the new service. Each of these changes for Route 9/X9 is discussed below.
Change in Headways
As discussed in section 3.2.1, the combined headway of Route 9 and X9 was slightly increased
with respect to Route 9 prior to the implementation of the limited-stop services. The Route 9
headway after the implementation of Route X9 in the peak periods is between 8.5 and 12
minutes and the Route X9 headway is 12 minutes as shown previously in Table 3-1.
Change in Headway Coefficient of Variation
The introduction of Route X9 increased the overall reliability of the service. Figure 3-7 shows
the Coefficient of Variation (COV) squared for both peak periods, by direction. In all cases, the
COV improved with the local and limited-stop services. The combined service (Route 9 and
Route X9) in fall 2007 showed a small improvement in the COV compared with fall 2005, even
though buses on the two routes now travel at different speeds and the combined headway is not
scheduled to be uniform along the route.
I
NB PM Peak Headway COV 2 (2005)
NB PM Peak Headway COV2 (2007)
Distance along Route
Distance along Route
-
Local -
X
Choice
i
SB PM Peak Headway COV 2 (2005)
SB PM Peak Headway COV2 (2007)
Distance along Route
Distance along Route
-
Local
-
X
Choice
Figure 3-7 Headway variation before and after implementation of Route X9
Table 3-5 shows the average headway COV 2 for the Ashland corridor before and after the
implementation of Route X9 with improved performance for both the local and X services.
Table 3-5 Headway Variability for Routes 9 and X9
AM Average Headway COV 2
2005
2007
NB
SB
NB
SB
9 0.68 0.48 0.25 0.40
019 0.24
X9
Both
0.68
0.48
0.58
0.69
PM Average Headway COV 2
9
X9
Both
2005
NB
SB
0.60 0.85
2007
NB
SB
0.45 0.61
0.60
0.66
0.85
0.26 0.53
0.88
Change in Speed
The average bus speed in the corridor increased after the implementation of Route X9 with the
local service speeds unchanged while the limited-stop services were on average 23% faster.
Figure 3-8 shows the speed profiles by direction and time period in the Ashland corridor before
and after the implementation of Route X9.
NBAM Peak
NB PM Peak
14
0.
E 12
-..
10
a.
0.
0
20
15
10
5
0
5
Distance (Miles)
-*9
10
15
20
Distance (Miles)
(2007) -m--X9 (2007) -
-- *--9 (2007) -.-
9 (2005)
*X9 (2007)
-9
(2005)
i
SB AM Peak
0
5
SB PM Peak
20
15
10
0
5
X9 (2007) -
15
20
Distance (Miles)
Distance (Miles)
-- +--9 (2007) -
10
9 (2005)
-4
9 (2007) -"--X9 (2007)
-9
(2005)
Figure 3-8 Speed profiles before and after implementation of Route X9
Table 3-6 presents the average speeds during the peak periods in both directions before and after
the implementation of Route X9.
Table 3-6 Average Speeds before and after the implementation of Route X9
AM Peak
2007
2005
SB
NB
SB
NB
9 '-9:79 9.56 9.91
X9
9.33
11-64 12.03
PM Peak
2007
2005
SB
NB
SB @g
91 935 8.38.ilIl
X9 '.n
8.41
11.15
Results
After computing the changes in speed, headway, and headway coefficient of variation, the
change in passenger travel time (including access, egress, waiting, and travel time) can be
modeled for all passengers using the method proposed by Schwarcz.
The change in passenger travel time was estimated for different lengths (i.e. 0-1 miles, 1-2 miles,
etc.). The approach used was to average the change in travel times (obtained from the Schwarcz
model) for all the OD pairs weighted by their demand. The advantage of this approach is that it
only accounts for the changes in travel time in OD pairs with some demand and that it gives
greater weight to the impacts shown on the most heavily used patters.
Figure 3-9 shows the changes in travel time for the passengers for Route 9/X9 by time period
and direction. As expected, longer trips have larger time savings.
NBPM Peak
0
E
0
,
-E
SBPM Peak
0
6
4
-2-
8
10
12
-S
X.E
-10 --
,
0
2
4
6
8
10
12
-2-
10 Trip Length (miles)
Trip Length (miles)
Figure 3-9 Changes in travel time weighted by demand
112
271,T2.7
2.0
6
2.52.5
Table 3-7 shows the change in average travel time for Route 9/X9 by direction and time period.
Table 3-7 Average Travel before and after Route X9 implementation
AM Peak (min)
2005
2007
NB SB
NB:
SB
22
3.2.4
22.71
2005
24
PM Peak (min)
2007
SB
SB
Travel Time Elasticities
Travel time elasticities were computed for different trip lengths based on the results from the
percentage change in ridership (see Figure 3-6) and the percentage change in travel time (see
Figure 3-9). Figure 3-10 shows the resulting travel time elasticities for Route 9/X9 by direction
and time period. The figure also shows the results for Route 54/X54 during the AM period in the
NB direction where a similar analysis to Route 9/X9 was applied.
Figure 3-10 Travel Time Elasticities by Trip Length
When the computed elasticities are averaged for different trip lengths, a decreasing trend is
observed as trip length increases, meaning that, more long trips than short trips will be generated
given the same percentage change in passenger travel time. This result is not surprising since it is
reasonable that, for example, a 5% reduction in travel time for a 10-minute (i.e. 30 seconds) trip
will attract fewer new riders than a 5% reduction for a 100-minute trip (i.e. 5 minutes).
Figure 3-11 shows the computed average travel time elasticities together with the elasticities (in
blue) estimated from running a linear regression on the average values. The values obtained from
the estimated model from the linear regression, shown at the right of the figure, will be used for
predicting ridership changes for new limited-stop services in Chicago in Chapter 6.
Note that the evidence from the Ashland and Cicero corridors in Chicago shows a zero elasticity
for trip lengths between zero and one mile given the small changes in travel time (less than 1.5
minutes) after the implementation of Routes X9 and X54 (see Figure 3-9). Zero elasticity for
demand forecasts implies that a change on level of service (measured as a change in travel time)
will not impact demand. As such, the use of zero elasticity should be viewed with caution. For
service configurations that lead to small changes in travel time (less than 1.5 minutes) on the 0-1
mile trips, zero elasticity can be safely used; however, for service configurations that lead to
significant changes in travel times, the use of a -0.71 elasticity is recommended (i.e. the elasticity
used for 1-2 mile trips).
0
Trip Length (ml)
-2
-3
-4
Elasticity
0 to 1
1 to 2
0.00
-0.71
2 to 3
3 to 4
More than 4
-1.42
-2.14
-2.85
Trip Length (Miles)
Figure 3-11 Average Travel Time Elasticities by Trip Length
3.3
Ridership change when implementing BRT Limited-Stop Services
As described in section 2.2, BRT services have been shown to attract up to 33% more ridership
purely due to branding. The methodology proposed to accounting for the BRT branding effect is
that proposed in TCRP Report 118 (2007). This method suggests how different BRT elements
including running ways, stations, vehicles, service applications, and Intelligent Transportation
System (ITS) can be used to calculate a factor that in the best case can add an extra 25%
ridership to the estimates obtained from the travel time elasticities proposed previously in this
chapter.
The proposed methodology can be divided into four steps. The first step is to select and add the
BRT points for any components included in the proposed new BRT service, as shown in Table
3-8, to obtain the subtotal. The second step is to add the synergy points; if the subtotal points are
greater than 60, the synergy points (15 points) are added, and if not, no points are added. The
third is to add the subtotal and the synergy points to obtain the total points. The fourth step is to
multiply the total points by 0.25 and that number is the percentage increase in ridership (with
respect to the base line riders that choose the BRT service over the local service) due to the
branding effect.
Table 3-8 Additional ridership impacts of selected BRT components
COMPONENT
Running Ways (not additive)
Grade-separated busway (special right-of way)
At-grade busway (special)
Median arterial busway
All-day bus lanes (specially delineated)
Peak-hour bus lanes
Mixed traffic
Stations (additive)
Conventional shelter
Unique/attractively designed shelter
llumination
Telephones/security phones
Climate-controlled waiting area
Passenger amenities
Passenger services
Vehicles (additive)
Conventional vehicles
Uniquely designed vehicles (external)
Air conditioning
Wide multi-door configuration
Level boarding (low-floor or high platform)
Service Patterns (additive)
All-day service span
High-frequency service (10 min or less)
Clear, simple, service pattern
Off-vehicle fare collection
ITS Application (selective additive)
Passenger information at stops
Passenger information on vehicles
BRT Branding (additive)
Vehicles and stations
PERCENTAGE
20%
20%
15%
10%
5%
0%
0%
15%
0%
2%
2%
3%
3%
3%
2%
15%
0%
5%
0%
5%
5%
15%
4%
4%
4%
3%
10%
7%
3%
10%
7%
Source: TCRP Report 118
3.4
Summary
This chapter proposed a method for measuring and estimating the route/corridor changes in
ridership when a limited-stop or a BRT service is introduced overlapping with the existing local
service. The method accounts for the ridership changes due to corridor service improvements and
branding.
The service improvements are captured through passenger travel time changes (including access,
egress, waiting, and in-vehicle) and travel time elasticities. The chapter calculates travel times
elasticities for two corridors in the city of Chicago. The elasticities found in this section can be
safely used for predicting ridership changes in corridors with characteristics similar to Chicago;
however, for predicting ridership changes in other US cities it is strongly recommended first to
estimate city-specific elasticities and use these values in forecasting.
Finally, the chapter proposes using the methodology described in the TCRP Report 118 (2007) to
account for the ridership increases due to branding. These increases should only be included
when a BRT system is being implemented.
4
BUS SERVICE CHOICE: UNDERSTANDING PASSENGER BEHAVIOR
Chapter 2 pointed out some limitations of the Schwarcz model for evaluating limited-stop bus
services overlapped with local bus service. One of these limitations is how the model assigns
passenger demand between the two services. Schwarcz (2004) used a deterministic approach
based on all passengers on a specific OD pair choosing the strategy (wait for the local service,
wait for the limited-stop service, or take the first service that comes) that minimizes their
weighted travel time. In other words, the model performs an all-or-nothing assignment based on
the estimated total travel time. However, it is well known that passengers have different
preferences and that a probabilistic assignment that takes into account these differences would be
more realistic.
This chapter develops a probabilistic approach, built on a path-size logit model, to assign
demand to the limited-stop (or BRT) services and the overlapping local service. The chapter is
divided into four sections. The first section describes the conceptual framework. The second
section lays out the proposed modeling approach. The third section analyzes passenger's bus
service choices in Chicago based on a customer behavior survey carried out on overlapping
limited-stop (X-Services) and local service. The last section estimates a logit model for Chicago
using Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data, and
customer surveys. The parameters of the logit model and the results of surveys shown in this
chapter can be safely applied to Chicago corridors; however, for other cities, it is strongly
recommended applying the techniques proposed in this chapter to analyze passenger's bus
service choices and to obtain city-specific parameters for the logit model.
4.1
Conceptual Framework
Individuals choose among alternatives based on this personal characteristics and preferences and
on the alternative's attributes. Therefore, bus service choice modeling should be based on these
characteristics and preferences rather than on simply minimizing the passengers' weighted travel
time. A logit model can include the passengers' characteristics and the alternatives' attributes
and should give better assignment results than a deterministic assignment.
At first glance a binomial logit model could model the passengers' choice between local and
limited-stop service. However, the choice set that passengers have includes more than two
alternatives. Specifically, passengers choose between three alternative strategies rather than
between two services. The first strategy is to walk to their closest stop (either a local or a
combined stop) and wait for the local bus; a customer who selects this alternative will be referred
to as Local Preferred.The second strategy is to walk to the combined stop and wait for the
limited-stop service; a customer who selects this alternative will be referred to as Limited
Preferred.The third strategy is to walk to a combined stop and take the first service that comes
(either a local or a limited-stop service); a customer who selects this alternative will be referred
to as Choice. The characteristics of the third alternative are a combination of the other two as
shown below:
(4-1)
ATFirstBuis
=ATLimited
ETFFtBuS
= F - ETLiited + (1
WTHFstBus
IVTFirstBus
FirstBus
2
= (1
-
F) - ETLoc
(43)
[1 + COV 2FirstBs
F) - IVTChoiceLoc + F
(4-2)
IVTLimited
(4-4)
WhereATFirstBus is the access time for a Choice customer
ATLimited is the access time for a Limited Preferred customer
ETFirstBus is the egress time for a Choice customer
ETLimited is the egress time for a Limited Preferred customer
WTFirstBus is the expected waiting time for a Choice customer
HFirstBusis the combined headway of the local and limited-stop service
COVFirstBus is the Headway Coefficient of Variation of the combination of local and
limited-stop service
IVTFirstBus is the in-vehicle time for a Choice customer
IVTChoiceLoc is the in-vehicle time for a local preferred customer from the closest
combined stop
IVTimited is the in-vehicle time for a limited preferred customer
F, is the limited frequency share
A multinomial logit model, such as the one presented in Figure 4-1, is not appropriate for this
choice context because the third alternative is not independent of the other two; therefore, it
cannot be treated as a single alternative being subject to the "red bus/blue bus paradox"
discussed by Ben-Akiva and Lerman (1985).
Limited-Stop
First Bus
Local
Figure 4-1 Multinomial Logit Model
A cross-nested logit model, such as the one presented in Figure 4-2, where the first-service-thatcomes alternative is a combination of the other two alternatives could solve this problem.
Limited-Stop
First Bus
Local
Figure 4-2 Cross-Nested Logit Model
However, there are two problems with the cross-nested approach. The first is that the correlation
between the wait-for-the-local strategy and the take-the-first-service-that-comes strategy is
different when a passenger is closer to a combined stop rather than a local stop. Figure 4-3 shows
a possible way to solve this problem by grouping the alternatives according to the nearest bus
stop (local or combined). Despite the fact that the diagram shows four alternatives (wait for the
local service at the local stop, wait for the local service at the combined stop, wait for the
limited-stop service at the combined stop, or take the first service that comes) each passenger
actually has a maximum choice set of three (or two 0 ) alternatives since the wait-for-the-local
alternative depends on where the passenger lives and is the only alternative at the local stop.
10When a passenger lives in the vicinity of a combined stop and wants to travel between and Origin and Destination
served by a limited-stop service, the choice set is reduced to two alternatives (waiting for the limited-stop service or
taking the first service that comes) since waiting for the local service is not a reasonable alternative.
Figure 4-3A shows the case of a passenger being closest to a local stop and Figure 4-3B shows
the case of a passenger being closest to a combined stop.
A
Local
B
Limited-Stop
First Bus
Limited-Stop
First Bus
Local
Figure 4-3 Alternative Cross-Nested Logit Model Structures
The second problem with the cross-nested logit model is that the wait-for-the local and wait-forthe-limited-stop alternatives are not independent. For example, if a passenger lives closer to a
combined stop, the access times for the local and the limited-stop alternatives are correlated, as
are the waiting times. In summary, the access, egress, waiting, and in-vehicle times of the three
alternatives are correlated, which makes the alternatives not independent.
4.2
Proposed modeling approach
The bus service discrete choice model with the three (correlated) alternative strategies is similar
to the discrete choice model in multi-modal networks. In these networks the individual faces
multiple choices such as main mode, boarding nodes, access modes, egress modes, etc. For
example, an individual can make the same complex trip in different ways such as bike to a metro
station, take a train, and then walk to her final destination; or walk to a metro station, take a train,
and then take a bus; or walk to the metro station, take a train, and then walk. Bovy et al.(2005)
propose defining different choice sets of multi-modal routes that have large path overlaps
between choices. In the previously example, the choice set is composed of three alternatives in
which each use a different mode (i.e. walk, bus, metro, bike) and where they partially overlap
with each other (e.g. an overlap in walking or biking to the metro station, an overlap in some
portion of the train times, and some overlap during the final walking stage).
The overlap between alternatives in the multi-modal network is similar to the problem outlined
here for bus service choice where the three alternatives display correlated access, egress, waiting,
and in-vehicle times. Ben-Akiva & Bierlaire (1999) and Ramming (2002) introduced a path-size
(PS) factor that reduces an alternative's disutility in the case of overlap that can be used both in
the multi-modal network problem and the route' 1 choice problem. Equation (4-5) shows the
proposed formulation for estimating the probability of alternative i being chosen from a choice
set C. If all links on an alternative i are unique, the path-size factor (PS) is equal to 1, and thus
the disutility of the alternative is unchanged. If alternative i overlaps with other alternatives, the
path-size PS is smaller than 1, and thus the disutility of the alternative increases while its relative
attractiveness decreases.
exp(V +1nPS, )
PUi ICQ
" exp(Vj +I n PSj,)
(4-5)
jecc
Where Vi is the disutility of alternative i
PSi is the Path Size correction factor for alternative i and individual n
CiQ are the available alternatives for individual n
The path-size factor presented by Bovy et.al (2005) and used by Ramming (2002) is the
exponential path-size formulation which is presented in Equation(4-6).
Psi=
/
1
L.
aErL,
jE=C,,
where: PSi
1i =
la =
Jai
(4-6)
Lj
Path size factor for alternative i. It should be between 0 and 1.
Set of legs of route alternative i
The length of leg a
Li= The total route length for alternative i
Lj =The total route length for alternative j
Cin= Set of different routes
" The definition of route choice in this context refers to the decision of path or trajectory between two points.
6
aj
=
y =
(Binary) Element of the assignment corresponding to alternative a and alternative
j. If a leg a is unique (only used in alternative i), then
6ai =1 and Saj = 0 Vjwi
Scaling parameter ( 0).
Bovy et al. define the route legs (Ti) in different ways (i.e. time, length and spatial). Figure 4-4
provides an example of how to define the path-size factor. As shown, a passenger traveling
between A and B can choose between three overlapping alternatives. The first alternative has 3
legs and consists on walking from I to II, taking the train from II to III, and then taking a bus
from III to IV. The second alternative has 3 legs and consists on walking from I to II, taking the
car from II to III, and then taking the bus from III to IV. The third alternative has 2 legs and
consists in walking from I to 1I, and then biking from II to IV.
15 min/leg 2
5 min/leg 1
5 min/leg 1
A2.
4 min/leg 3
10 min/leg 2
4 min/leg3
B
5 min/leg 1
20 min/l eg 2
Figure 4-4 Example of Path-size definition
Figure 4-4 also shows the overlap between alternatives during some portions of the trip. For
example, all the alternatives overlap in the walk from I to II, and alternatives 1 and 2 overlap in
the bus from III to IV. These overlaps can be defined either as time, length, or leg. Equations
(4-7) and (4-8) show the path-size factor for alternative 1 in the case of time overlap and leg
overlap respectively.
5
PS =--.
24 (24y
24
PSI=
15
11_________________
24)
)19
+24y
(25)
1
3
1
+--24 (24>7
4
+ -24 (24
24
1
1
1
1
.'
(24>
24
(47
(
29
1
-3) 3( 7-1
+ -1
(4-8)
The challenge in the bus service choice problem is defining the legs and the Path Size factors for
each three strategy. In this problem, each strategy includes four segments (access, waiting, invehicle, and egress) that can be divided into legs that overlap with each other. The definition of
these overlaps is given below.
Access time
The access time for the three defined strategies overlaps in the walking time to the local stop,
since the model assumes that passengers going to a combined stop have to pass by the local stop.
Figure 4-5 shows how the access time can be divided into two legs. The three strategies (wait for
the local, wait for the limited-stop, or take the first bus that comes) overlap in the leg that
represents the walking time to the local stop (ALoC)-
Lc ALim~ALoc
Origin
0 Local stop
* Combined stop
Figure 4-5 Access Time Diagram
Based on Figure 4-5, Equations (4-9) through (4-11) present the number of legs into which the
initial access time of the three alternatives can be divided. The local alternative has 1 leg, the
limited-stop alternative has 2 legs, and the first-that-comes alternative has 2 legs.
(49)
ALoc =ALoc
Am
=
ALoc + [ALim
A FirstBus
ALoc
(4-10)
- ALoc
+ [ALim
-
ALoc
(4-11)
Access time for the local alternative
ALim = Access time for the limited-stop alternative
AFirstBus = Access time for the first-bus-that-comes alternative
where:ALoc=
Waiting time
Waiting times can be expressed similarly to access times. It can be assumed that the minimum
expected waiting time is the one for the take-the-first-bus-that-comes strategy and that the
expected waiting times for the wait-for-the-local and wait-for-the-limited strategies can be
expressed in terms of the expected waiting time for the take-the-first-bus-that-comes strategy.
Equations (4-12), through (4-14) present the number of legs into which the waiting time of the
three alternatives can be divided. The local alternative has 2 legs, the limited-stop alternative has
2 legs, and the first that comes alternative has 1 leg. All the three alternatives share the WFirstBus
leg.
WTLoc = WTFirstBus +
[WTLOc
-
WTFirstBus
(4-12)
+
[WTLim
-
WTFirstBus
(4-13)
WTLi, =
WTFirstBus
WTFirstBus = WTFirstBus
(4-14)
where: WTLOc = Waiting time of the local alternative
WTLim = Waiting time of the limited-stop alternative
WFirstBus = Waiting time of the first-bus-that-comes alternative
In-Vehicle time
The approach for expressing in-vehicle times is similar to that used previously for access and
waiting times. Figure 4-6 shows the In-Vehicle Time diagram where the overlap between the
three alternatives for a trip from A to B can be observed. The figure shows how a person taking
the limited-stop service will board and alight at combined stops that, in the example, are different
than A and B. The in-vehicle time for the take-first-that-comes strategy is between the time on
the local and the time on the limited-stop.
TFirstBus
JVTLoc
A
B
*
Local stop
0 Combined stop
Figure 4-6 In-Vehicle Time Diagram
The In-Vehicle time of the local strategy can also be expressed as shown in Figure 4-7.
IVTLoc*
10
*
IVTChoioeLoc
I\/TLoc
@0G
A
0
P
0a
B
* Local stop
" Combined stop
Figure 4-7 In-Vehicle Time for the Local Strategy
Equations (4-15), through (4-17) present the approach for expressing In-Vehicle times.
IVTLoc = IVTLim +IVTLoc - IVTLim = IVTLim + IVTChoiceLoc +IVTLOC* -IVTLim
IVTLin = IVTLim
IVTFirstBus
=IVTLim F + IVTChoiceLoc (1 - F)
(4-15)
(4-16)
(4-17)
where: IVTLoc = In-vehicle time local alternative
IVTLimn = In-vehicle time limited-stop alternative
IVTFirstBus =In-vehicle time first-bus-that-comes alternative
IVTChoiceLoc = In-vehicle time in a local service from the origin closest combined stop
IVTLOC* = In-vehicle time in local service from the origin closest local stop to the closest
F, is the limited frequency share
These equations can be rearranged as in Equations (4-18) through (4-20) where the number of
legs into which the in-vehicle time of the three alternatives can be divided are presented. The
local alternative has 6 legs, the limited-stop alternative has 2 legs, and the first that comes
alternative has 2 legs. Different legs are shared between alternatives.
IVTLOC = IVTLim F + IVTLim (1 - F)+ IVTChoceLoc (1 - F) + IVTLoc* (1 - F) +
(IVTChoiceLoc +
Loc
)F
-
IVTLim
IVTLim = IVTLimF + IVTLim (1 F)
IVTFirstBus
(4-18)
=IVTLm F + IVTChoiceLoc (1- F)
(4-19)
(4-20)
where: IVTLoc = In-vehicle time local alternative
IVTLim = In-vehicle time limited-stop alternative
IVTFirstBus = In-vehicle time first-bus-that-comes alternative
IVTChoiceLoc = In-vehicle time in a local service from the origin closest combined stop
IVTLOC* = In-vehicle time in local service from the origin closest local stop to the closest
origin combined stop
F, is the limited frequency share
Egress Time
The egress time is overlapped in the walking time from the combined stop to the local stop, since
the model assumes that passengers on the limited-stop service alight at the combined stop and
pass by the local stop. Figure 4-8 shows the Egress Time Diagram. The three strategies (wait for
the local, wait for the limited-stop, or take the first bus that comes) overlap in the leg that
represents the walking time to the local stop.
Destin ation
ELoc
0
EILim-ELoc
O Local stop
* Combined stDp
Figure 4-8 Egress Time Diagram
Equations (4-21) through (4-23) present the approach for expressing Egress times.
ELoc = EL
ELim
= ELoc +[ELim
EFirstBus
(4-21)
- ELoc
= ELoc (1 - F) + ELim F
(4-22)
(4-23)
where: ELoc = Egress time local alternative
ELim= Egress time limited-stop alternative
EFirstBus = Egress time first-bus-that-comes alternative
F, is the limited frequency share
These equations can be rearranged as in Equations (4-24) through (4-26) where the number of
legs into which egress time of the three alternatives can be divided, is presented. The local
alternative has 2 legs, the limited-stop alternative has 5 legs, and the first-that-comes has 2 legs.
Different legs are shared between alternatives (the ELoc(1-F) leg is shared across all strategies,
the ELoCF leg between the local and the limited strategies, and the ELimF leg between the limited
and the first bus strategy).
ELoc = ELoc (1 - F) + ELoc F
(4-24)
ELim
ELoc (1 - F) + ELOc F + ELim F + ELim (1- F) - ELc
=ELoc (1
EFirstBus
where: ELoc
=
(4-25)
(4-26)
F)+ ELim F
Egress time local alternative
ELim = Egress time limited-stop alternative
Egress time first bus that comes alternative
F, is the limited frequency share
EFirstBus =
The four segments (access, waiting, in-vehicle, and egress) sum to produce the total passenger
time alternative as shown in Equations (4-27) through (4-29). The path-size correction factor for
the three alternatives can be developed based on the previously defined legs and computed using
the Equation (4-6).
ELoc
(4-27)
TTLim -ALim + WTLi, + IVTir + ELim
(4-28)
TTLOC
=ALoc
TTFirstBus
+ WTLOc +
AFirstBus
IVTLOC +
+ WTFirstBus ±IVTFirstBus +E FirstBus
(4-29)
CTA Customer Behavior Survey
4.3
A small survey was conducted of passengers riding buses in Chicago (the Ashland and Cicero
corridors) to understand how passengers choose between local and limited-stop services and to
estimate a path-size logit model for assigning demand between these services in Chicago
following the methodology described in the previous section.
4.3.1
Survey Process
The survey questions included:
"
Whether the passenger had waited for local service, limited-stop service, or took the first
service that came
* Whether or not the passenger was familiar with the X-Service
* Trip purpose
e
If the combined stop was her closest stop
" Number of blocks between origin (and destination) to the closest local and combined stop
e
The origin and the destination of her trip
Three different versions of the survey were designed and distributed according to the service
(local and limited-stop) and the stop where passengers boarded (local or combined stop). The
surveys are included in Appendix A.
The survey was conducted in March 2009 on the Ashland (Route 9/X9) and Cicero corridors
(Route 54/X54). The survey procedure consisted of splitting the 4-person team into two teams.
The first team, which had three members, administrated surveys on local services, and the
second team, with one member, administrated surveys on the limited-stop services. The surveys
were administrated simultaneously on both service types on each corridor. The reason for having
the larger group on the local service was that for this service, two versions of the survey were
administrated depending on whether the passenger boarded at a local stop or at a combined stop.
One member kept track of the stops on the route and told the other team members which survey
to hand out, at which stop passengers boarded and at what time the survey was carried out. The
other two members handed out the survey to the customer, assisted them with questions about
the survey, filled out the survey with customers' answers in some cases, or translated the survey
into Spanish when necessary.
4.3.2
Field Observations
During the onboard survey, the following observations were made:
* X-service buses did not always attempt to overtake local service buses, instead they
simply trailed behind the local service. This is not the desired operating plan and can be
especially irksome for the limited-stop preferred passengers (the ones who wait for the Xservice) who will not see any benefits from the extra waiting and walking to a combined
stop when this happens. Explicit instructions should be given to drivers to ensure that Xservice buses always overtake local service buses when possible.
"
Bus bunching appeared prevalent. As a result of the previously mentioned phenomenon,
buses were observed to bunch for long periods.
* The local service was usually more crowded than the X-service.
" Most boardings occurred at combined stops. Local services serve few passengers at local
stops while X-services and local services serve high passenger demand at combined
stops.
* In both corridors, there is a large population of Spanish speakers so any change in service
should be publicized in both English and Spanish.
4.3.3
Data Analysis
A total of 182 completed surveys were obtained including 133 on Route 9/X9 (carried out during
the morning), and 49 on Route 54/X54 (carried out during the afternoon). Three versions of the
survey were administrated based on the service used (local or limited-stop) and the stop where
passengers boarded (local or combined stop). Table 4-1 shows the number of surveys completed
by stop and service used. Of the total 182 surveys, only 145 were judged to be valid since some
were incomplete or the information provided did not appear to be consistent (e.g. a passenger
stating that she always waits for the X bus and then stating that she first goes to the local stop
and if she does not see the local bus coming, she starts walking to the combined stop.)
Table 4-1 Number of surveys by type
9/X9
54/X54
Total
34
12
67
26
32
11
133
49
43
On both corridors, more than 80% of the respondents ride the route more than 3 days a week. As
expected, during the morning (when surveys in Route 9/X9 were carried out) most people were
going to work or school and during the afternoon (when surveys in Route 54/X54 were carried
out) most people were either returning home or going to work (see Figure 4-9).
How many days a week do you ride
Route 9/X9?
6%
11%
11%27%
What is the purpose of the trip you are
making now? Route 9/X9
15%
4%o17
9%
MHome
M 7days
0 5 to 6
15%
U School
15%wo rk/Work-related
0 Shopping
1
ME1to 2
O Less than 1
N Social/Recreational
E Ho spital/M edical
0 Other
41%
49%
How many days a week do you ride
Route 54/X54?
What is the purpose of the trip you are
making now? Route 54/X54
7%
10%
12%
1%28%
43%
4%
0 7 days
*7dys120X
0 5 to 6
0 5 to 6
* to 2
01 Less than 1
M Home
MSchool
Sho
0 Wo rk/Wo rk-related
0Shopping
L So cial/Recreatio nal
2
3%-/
MHospital/M edical
0 Other
6%
0
38%
Figure 4-9 Frequency of use and trip purpose during the morning in the Ashland and Cicero corridors
On both routes, 55%-57% of the riders were female, and 12%-16% of the riders had some
difficulty walking long distances.
With the information provided by the respondents, it is possible to establish, at the stop level,
which were local preferred, limited-stop preferred or choice passengers. Figure 4-10 through
Figure 4-12 present the split of demand between these three categories at the stop level for Route
9/X9 and Route 54/X54.
Figure 4-10 Strategy by local service passengers boarding at a local stop
Route 541X54. ( N22)
Route 9/X9. (N=59)
7.5%
80%
80%
60%
60%
40%
40%
27%
25%
20%
20%
0%
0%
Always take the local
service
Always take the local
service
Take the first bus that
comes
Take the first bus that
comes
Figure 4-11 Strategy by local service passengers boarding at a combined stop
Route 54/X54. (N=10)
Route 9/X9. (N=31)
80%
80%
60%
60%
0% 40%
20%
7o
19%
Always take the X service
30%
20% -
Take the first bus that
comes
Always take the X service
Take the first bus that
comes
Figure 4-12 Strategy by limited-stop passengers boarding at a combined stop
In order to estimate the overall split between the three strategies, it is also necessary to obtain the
demand split between the local and the limited-stop services and between local and combined
stops. This information was obtained from the Automatic Passenger Count (APC) and Automatic
Vehicle Location (AVL) data and is presented in Figure 4-13.
Route 9/X9. AM Demand Split
34%
o
38%
Route 54/X54. PM Demand Split
Local at Local
Stop
[ Local at Local
Stop
4
I Local at
Combined Stop
o X at Combined
0 X at Combined
Stop
Stop
28%
Local at
Combined Stop
19%
Figure 4-13 Demand Split between services and stops
It is also important to recall that despite the fact that a choice passenger was earlier defined as
someone who goes directly to a combined stop and takes the first bus that comes, it was found
that 16% of all passengers first go to the local stop and if they do not see the local bus then walk
to the combined stop. Of those passengers 75% ended up taking the local bus at the local stop.
Given that the purpose of the modeling process is to correctly assign passengers to strategies
(and later to services) passengers who took the local bus at the local stop and claimed that "they
go to the local stop and then to the combined stop" were classified as local preferred.
The demand split between the three strategies can be obtained by combining the survey results
with the AVL and APC information. The overall split between the three strategies, for both
corridors, is presented in Figure 4-14. From these results it can be seen that about a third of the
riders are choice (take the first service that comes) and that only a small percentage (7% for
Route 9/X9 and 14% for Route 54/X54) are limited preferred. Among the explanations for the
low percentage of limited preferred customers are the frequency share of the limited-stop service
(50% or less in some cases 12), a larger weight for waiting and access time compared with invehicle time, short trip lengths, the number of X-services bunched behind local services,
difficulty for some passengers in walking, and the lack of familiarity of some customers with the
X-services.
Figure 4-14 Market shares in the Ashland and Cicero Corridors
From the survey data it is also possible to estimate, as shown in Figure 4-15 through Figure 4-17:
what percentage of passengers start their trips closest to a local stop; what percentage of
passengers living closer to a local stop walk first to a local stop and then to a combined one;
what percentage of passengers walk directly to the combined stop; what percentage of
passengers are not familiar with the X-services; and what percentage of the passengers who take
the first bus that comes (choice) wait for the X service if they see it coming behind the local
service.
12 The
CTA provides headways of 9 to 12 minutes for the local service and 13 minutes for the limited-stop service in
SB direction in the Ashland corridor.
80%
70%
-
50%
40%
30%
10%%
0%
Go to the local stop and then to the
combined stop
Always take the local service
Figure 4-15 Demand split of local service passengers boarding at a local stop (N=39)
80%70%
60%
50%
40%
Take the fist buszthat comes
isee the X
bs coming behidlca
28Y
30%
buen
20%
bu
10%
Always take the local service
Take the first bus that comes
t ais
b fo
he
b ,n
|Wa
Take the first bus that comes
Figure 4-16 Demand split of local service passengers boarding at a combined stop (N=81)
80% -
70%60% 50% 40% 47YTake the fist bus that comes
if see te Xbus comng behinda
but
30% 20% 10%0%-
Always take the X service
Take the first bus that comes
Take the first
bus that
comes
Figure 4-17 Demand split of limited-stop service passengers boarding at a combined stop (N=41)
With the survey results combined with the APC and AVL data it is also possible to establish that
for both corridors:
* At least 13% of the passengers are not familiar with the X-service. An additional 3% of
the surveyed passengers, who stated they are familiar with the X-service, also stated that
they always wait for the local service despite travelling between stops served by the
limited-stop service.
*
22% of the passengers take the first service that comes although they wait for the Xservice if can see it coming behind the local service.
e
37% of the riders live closer to a local stop.
0
13% of the riders go to a local stop and stay there, 16% of the riders first go to a local
stop but if don't see the local service coming they walk to a combined stop, and 8%of
the riders living closer to a local stop go directly to a combined stop.
From the previous information, we can identify an opportunity to switch some passengers from
the local to the limited-stop services by familiarizing customers with the X-services and
providing real time information about the next X-service arrival time.
Finally, it is possible to combine the AVL data with the survey data to obtain the total passenger
travel time with the selected alternative for each individual surveyed as well as the time that the
individual would have spent if she had chosen the other alternatives. The access time can be
obtained by converting the number of blocks passengers walked to the initial stop and from their
destination stop to their final destination to time by assuming a standard block distance and an
average walking speed. The expected waiting time and the in-vehicle time can be obtained from
the AVL data. By comparing the total travel time with the time of the alternative that would have
minimized the passenger travel time for each respondent 46% of passengers were found not to
have chosen the alternative that minimized their total travel time, which confirms the hypothesis
that the decision making process varies across individuals and that a probabilistic assignment is
more appropriate than a deterministic assignment.
Discrete Choice Model
4.4
As previously mentioned, with the information available it is possible to establish (for each
surveyed customer) the travel time (including access, egress, waiting, and in-vehicle time) for the
chosen alternative as well as the travel time for the other two alternatives. The procedure to
convert the surveyed data to access, egress, waiting, and in-vehicle times is presented below:
" The initial and final access time was obtained based on the number of blocks walked
assuming a block size of 400 feet and an average walking time of 250 ft/min (Transit
Capacity and Quality of Service Manual, 1999).
*
The waiting time was obtained based on the headway and headway coefficient of
variation (COV) for that route and period assuming random passenger arrivals over time.
For Route 9/X9 in the NB direction, the expected waiting time for the local preferred was
6.1 min., for the limited preferred 6.1 min., and for the choice 4.1 min. Southbound, the
expected waiting time for the local preferred was 5.3 min., for the limited preferred 6.7
min., and for the choice 4.3 min.
For Route 54/X54 in the NB direction, the expected waiting time for the local preferred
was 5.8 min, for the limited preferred 5.8 min, and for the choice 3.6 min. Southbound,
the expected waiting time for the local preferred was 5.9 min., for the local preferred 5.8
min., and for the choice 3.7 min.
e
The in-vehicle time was obtained based on the individuals stated origin and destination
converted to distance and then to time given the speed of the service.
For Route 9/X9 in the NB direction, the average speed for the local service was 9.8 mph,
and 11.4 mph for the limited-stop service. Southbound, the average speed for the local
service was 8.4 mph, and 12.0 mph for the limited-stop service.
For Route 54/X54 in the NB direction, the average speed for the local service was 9.7
mph, and 12.6 mph for the limited-stop service. Southbound, the average speed for the
local service was 9.8 mph, and 12.9 mph for the limited-stop service.
*
The survey observations needed to be weighted since the sampled demand split obtained
from the surveys does not correspond to the actual split that was obtained from the AVL
and APC data. If the model is estimated without weighting the observations, all the
parameter estimates would be consistent except the constants; however, by giving
appropriate weights to the observations all the parameters estimated are consistent.
In summary, a data set with the available alternatives, their characteristics, and the chosen
alternative was built in order to estimate different logit models.
As described in section 4.2, a path-size logit model (with a scale parameter y=1) is the approach
used to model the bus service choice as defined in Equations (4-30) through (4-32).
VLocal
=aLocal +
T±A TLocal
VFirstBus =aFirstBus + JOATTA
VLimited ~ 1AT
WT WTLocal
+
IVT
VTLocal
+ ln(PSLocaI)
FirstBus +PWT WTFirstBus + 9jvTIVTFrstBus
Limited + P+TI
Limited
ln(PSLimited
ln(PSFitBus
(4-30)
(4-31)
(4-32)
where: VLoc, VFirstBus,and VLimited are the disutilities for the wait-for-the-local-service, take-thefirst-service-that-comes, and wait-for-the-limited-stop-service alternatives respectively
aLoc is the alternative specific constant for the wait-for-the-local alternative
aFirstcomes is the alternative specific constant for the take-the-first-that-comes alternative
TATLocal, TATFirstBusand TATLimited are the total access times (including access and egress)
for the wait-for-the-local-service, take-the service-that-comes, and wait-for-the-limitedstop-service alternatives respectively
WTLocal, WTFirstBusand WTLimited are the expected waiting times for wait-for-the-localservice, take-the-first-service-that-comes, and wait-for-the-limited-stop-service
alternatives respectively
IVTLocal, IVTFirstBus, and IVTLimited are the in-vehicle times for wait-for-the-local-service,
take-the-first-service-that-comes, and wait-for-the-limited-stop-service alternatives
respectively
PSLocal, PSFirstBus, and PSLimited are the Path-size correction factors the wait-for-the-localservice, take-the-first-service-that-comes, and wait-for-the-limited-stop-service
alternatives respectively
The results of estimating the model are presented in Table 4-2.
Table 4-2 Parameters of the logit model
Coefficient
ASC Local
Value
3.45
t-stat
4.13
1.55
-0.366
-0.408
-0.693
1.35
-2.98
-1.43
-1.33
ASC Express
ASC First
B In-Vehicle Time
B Total Access Time
B Waiting Time
B PS
1I
2
Adj. p
L(null)
L(B)
0.366
-106.289
-62.367
From the results we can see that all the coefficients were significant and that if all the
alternatives have the same characteristics (access, egress, waiting, and in-vehicle time)
individuals prefer to wait for the local as their first choice, take the first bus that comes as their
second choice, and to wait for the limited-stop service as their last choice. The results of this
model were better than those obtained with a multinomial logit without the path-size correction
factor".13
As expected, out-of-vehicle time is more onerous than the in-vehicle time. However, the total
access time (access and egress) is less onerous than the waiting time which may be due to the
small sample size. It was found that changing these coefficients did not significantly change the
demand assignment. When the model was applied to assign passenger demand to new limitedstop services in Chicago (Chapter 6) the change in the coefficients (having a weight of 2 for
waiting time relative to in-vehicle time and weight of 3 for the total access with respect to invehicle time) did not change the demand split by more than 5%.
multinomial logit model without including the path size (PS) factor has an adjusted p2 of 0.339, a final loglikelihood of -65.262, and non statistically significance (at the 20% level) of the total access and waiting time
variables.
13The
5
PROPOSED METHODOLOGY
This chapter incorporates the improvements to the Schwarcz model that were described in the
previous two chapters and presents an improved overall methodology for modeling and
evaluating service configurations of limited-stop and BRT services overlapped with local
services. The chapter describes the model approach, the model specifications (inputs, OD matrix
estimation procedure, running time estimation procedure, and outputs), and measures for
evaluating limited-stop service configurations.
Model Approach
5.1
The model developed in this thesis is based on the previous model developed by Schwarcz
(2004). The model is used to evaluate a specific service configuration of a limited-stop or BRT
service overlapped with local service. In other words, the model does not find the best possible
configuration but rather describes the performance of a user-defined service configuration. The
model can be used either when limited-stop (or BRT) services are introduced overlapping with
existing local services or when a revised service configuration is being proposed (e.g., changing
combined stops, or upgrading a limited-stop to a BRT service).
For a specific bus route, the user can define the following service plan characteristics:
0 Bus stop locations (stop spacing).
0 Frequencies for the limited-stop and the local services. The term frequency share is used
here to refer to the percentage of total bus trips that are provided on the limited-stop
service.
*
BRT elements: Level of segregation from traffic, fare media and other elements of
enhanced boardings, and Transit Signal Priority (TSP).
For a specific user-defined service configuration for a bus corridor, the model describes, for a
time period, how it performs in terms of the following six measures:
e
Market Share: This refers to the split of passengers between local preferred, limited
preferred, and choice. This is an indicator of the customer willingness to take the limited-
stop service.
* Demand Split: This is the percentage of passengers that take the local and the limitedstop services. This is an indicator of the level of use of both services.
* Average passengers per trip: This is an indicator of the productivity of both services and
the evenness of load between them.
*
Change in running times/change in corridor speed: This refers to the change in speeds for
the "new" local and limited-stop services due to service enhancements (increases in stop
spacing, and other BRT elements). This indicator measures the benefit for the operator in
running time savings (that can reduce fleet and increase productivity).
" Change in passengers' travel time: The change in the passenger's average travel time
(including access, egress, waiting, and in-vehicle times) is an indicator of the benefit
received by the passengers in terms of actual travel time savings.
" Change in ridership: This is the change in ridership due to changes in passengers' travel
time and branding elements.
The model proposed in this chapter differs in three respects from the Schwarcz model:
*
The methodology to assign existing passenger demand on the corridor to the limited-stop
and the local services. The new model uses a probabilistic approach, based on a path-size
logit model, rather than a deterministic approach based on the assumption that all
passengers between a specific OD pair minimize the weighted sum of their expected
travel time when they choose a strategy (wait for the local service, wait for the limitedstop service, or take the first bus that comes).
" Newly generated demand as the result of the implementation of limited-stop services is
implemented including gains due to services improvements (travel time savings), and, in
the case of BRT services, branding.
" Besides estimating the changes in running time due to stop reduction, the model estimates
the other savings due to BRT elements (level of segregation from traffic, fare media and
enhanced boarding, and Transit Signal Priority) for the local and the limited-stop (or
BRT) services.
5.2
Model framework
Figure 5-1 presents the basic model framework. The modeling process is iterative and can be
split into four stages. The first stage requires entering the inputs including the service
frequencies, the passenger demand at the stop level for a specific period, the selected location of
the local stops and combined stops, the running times (which are a function of the passenger
demand split and are assumed in the first iteration), etc. The second stage computes the
corridor/route stop Origin-Destination (OD) matrix. The third stage computes market assignment
(the probability of each passenger waiting for the local service, waiting for the limited-stop
service, or taking the first service that comes); then assigns passengers to the local and the
limited stop service; and lastly computes the change in travel time for all the passengers and
estimates the changes in ridership due to these changes and due to branding (when BRT
components are included). The last stage consists of aggregating individual OD pair data to
obtain the outputs of the model for the corridor/route which include the six indicators, including
the passenger demand split, described in the previous section to evaluate limited-stop services
overlapped with local services. With the passenger demand split estimation obtained in the last
stage, the running times (from stage one) can be re-estimated and the procedure repeated until it
converges.
The four stages of the process are explained in detail in the next sections.
Model
1. Compute Utility with Travel
Time components (Access +
Waiting + In vehicle) for each
OD pair, for three possibilities
(Local, Limited, and First that
comes)
2.Market Classification. For each
OD pair travel time take the
probability for the 3 cases
3. Assignment.
. Stop assignment. Number of
passenger per stop
0 Route assignment. Number of
people local and limited
4. New ridership.
- Compute travel time changes
for different trip lengths
. Compute new riders based on
travel time elasticities and
current riders for each trip
length
If BRT. Add branding ridership
. Assign new riders proportional
to travel time savings
Figure 5-1 Model Framework
5.3
Model inputs
The inputs to the model are:
" Stop locations: The local stops are assumed to remain the same after the implementation
of the limited-stop service and a subset will be user-defined as combined stops (used by
both the local and limited-stop services).
* Distance between stops: This element is necessary for estimating the in-vehicle times as
well as the differential access times for the local preferred, the limited preferred, and the
choice customers.
* Frequencies and frequency share: Three frequencies must be defined for analysis
purposes: the local existing service prior to the implementation of the limited-stop
service, the new limited-stop service, and the "new" local service. Using the latter two
frequencies, the frequency share (the percentage of total bus trips that are provided on
the limited-stop service) can be determined. Furthermore, the frequency of the prior local
service is necessary for modeling the expected changes in waiting times.
Headway distribution -as reflected in the Headway Coefficient of Variation (COV)-: This
element is required, together with the frequencies, to model waiting times before and
after the implementation of limited-stop or BRT service. The model needs as inputs the
COV for the local service prior to the implementation of the new service (which can be
obtained from the AVL data), the COV of the new local service, the COV of the limitedstop service, and the COV of the corridor (the combination of the limited-stop and the
new local service). Through the validation of the Schwarcz model, it was found that the
combined services (local and limited-stop) COV could be approximated as the local
service COV prior to the implementation of the new service, that the COV of the limitedstop service could be approximated as one third of the COV of the corridor prior to the
implementation of the new strategy, and that the COV for the "new" local service could
be approximated as two thirds of the original COV.
*
BRT elements: The model requires defining if BRT elements, besides limited-stops, are
included in the new service specifically: the level of segregation of traffic, the fare media,
and the boarding and alighting characteristics. These elements are necessary for
estimating the running times and the ridership gains due to branding.
*
Boardings and alightings at each stop: The number of passengers boarding and alighting
by stop for each direction and time period are necessary for estimating an OD matrix and
for assigning passengers to strategies, stops, and services.
*
Disutility functions: The disutility functions of the three strategies passengers can choose
(wait for the local service, wait for the limited-stop service, or take the first service that
comes) are necessary for performing the probabilistic assignment. For cities other than
Chicago, it is recommended that the parameters of the utilities be estimated by following
the methodology proposed in Chapter 4 rather than using the parameters presented in that
chapter.
* Travel time elasticities: For cities other than Chicago, travel time elasticities should be
estimated using the methodology proposed in Chapter 3 rather than using the values
found in that chapter. The elasticities provide an estimate of the ridership changes due to
changes in passenger travel time (including access, egress, waiting, and in-vehicle time)
for different trip lengths.
*
Running times. Running times are necessary for estimating the in-vehicle times and
predicting the speed increases due to the various BRT elements. The next section
describes the methodology proposed for estimating running times for the proposed
services.
5.4
Running time estimation
The methodology proposed for estimating running times for a user-defined limited-stop service
(or change in a local service) has four steps: estimate base running times by type, compute
average movement speed, dwell time calibration, and estimate change in running times.
5.4.1
Estimate base running times by type
The first step is to assess the split of the running time into movement, dwell, traffic, and traffic
signal time. These components are defined as follows:
" The movement time is the time the bus is moving (even at a low speed).
e
The dwell time is the time the bus spends at stops serving passengers. This includes only
the time the bus has its doors open.
" The traffic time is the time the bus is not moving for various reasons including traffic
interaction and congestion (e.g. a cab stopping in front of the bus).
e
The traffic signal time is the time the bus is not moving due to a red light.
This split can be assessed from field observation or from detailed AVL data where the time spent
in traffic, traffic lights, dwell times, and movement can be identified throughout the
corridor/route.
This split of running time helps in determining what type of improvement would have the
greatest impact in the corridor. For example, a corridor with a large percentage of running time
waiting at signalized intersections could benefit greatly from implementing Transit Signal
Priority (TSP); or a corridor with a large percentage of the running time spent at stops (dwell
times) could benefit greatly from the implementation of enhanced boarding elements such as
low-floor buses, wider doors, off-vehicle fare collection, and level boarding.
Figure 5-2, from TCRP Report 26 (1997), shows typical time allocation for routes in the US for
different settings. The figure can be used as a guide, but data for the selected corridor should be
used when applying this process to specific routes.
15
15
cBo AVG
?//SUBURBJH
AVG
0
5
Travel Time, Minutes
Source: TCRP Report 26
Figure 5-2 Bus Travel Time Components
5.4.2
Compute average movement speed
The second step in estimating running times is computing the average movement speed. The
average movement speed reflects how other vehicles on the road affect the speed of the bus and
it can be estimated by dividing the route length by the movement time.
5.4.3
Dwell time calibration
The third step is the calibration of the dwell time which is based on the number of bus trips
serving the corridor in the study period, and assuming that demand is equally distributed among
these trips. The total dwell time of a bus can be described with Equation (5-1).
DT = ActualUsedStops -K + a -Boardings+ b -Alightings
(5-1)
Where DT is the total dwell time of a bus
ActualUsedStops is the average stops per trip made by the service (note that this is
different than the number of the stops along the corridor)
K is the constant that describes the time to open and close the doors
a is the coefficient for average boarding time per passenger
b is the coefficient for average alighting time per passenger
The values of a and b should come from a corridor assessment if possible or from Table 5-1. The
total dwell time modeled using the equation should be close to the actual dwell time estimated in
step 1.
Table 5-1 Passenger service times with single-channel passenger movement
Passenger Service time (s/p)
Situation
Observed Range Suggested Default
BOARDING
Pre-payment*
2.25 to 2.75
2.5
Single ticket or token
3.4 to 3.6
3.5
Exact change
3.6 to 4.3
4.0
Swipe or dip card
4.2
4.2
Smart card
3.0 to 3.7
3.5
ALIGHTING
Front door
2.6 to 3.7
3.3
Reardoor
1.4 to 2.7
2.1
Note: * includes no fare, bus pass, free, and pay-on-exit.
Note: Add 0.5 s/p to boarding times when standees present and
subtract 0.5 s/p from boarding and alighting times on low-floor
buses
Source: TCRP Report 100
5.4.4
Estimate change in running time
The last step is to estimate the changes in movement, dwell, traffic, and traffic signal times for
the new local and the limited-stop services as described below.
The change in movement time is estimated differently for limited-stop than for BRT services.
For limited-stop services, the change depends on the number of skipped stops' 4 , which according
to X-routes in Chicago is on average 35 seconds 5 . For BRT services, the change depends on the
level of lane segregation (right of way) and is modeled as the increase in the average movement
speeds. The average movement speed can be computed using the values presented in Table 5-2 note that the dwell time effect is treated separately
-.
Table 5-2 Base Running Times for Exclusive Bus Lanes
Stops per mile
Dwell Time
2
4
5
0s
2.06 min/mi
2.61 min/mi
2.94 min/mi
1
6
7
8
10
3.3 min/mi
3.72 min/mi
4.2 min/mi
5.34 min/mi
Note: The provided values assume no signal or traffic delays.
Note: For Arterial Roads with no traffic: Add 0.5-1.0. For Arterials with mixed traffic: Add: 1.0
Source: TCRP Report 100
The change in dwell time depends on the headway of the new local and limited-stop services, the
passenger demand for each service, and the fare media/enhanced boarding elements of the new
system. To estimate the dwell times it is necessary to repeat the procedure in step 2. First, the
number of bus trips operated on each service is determined. Second, the demand for each service
is estimated in the first iteration16 by assuming the demand split is equal to the frequency ratio of
the services. Third, the demand for the local (or the limited-stop) service can be assumed to be
equally split over the number of bus trips operated. The values of a and b can be taken from
This refers to the difference between number of average used stops by the local service and the number of actual
used combined stops by the limited-stop service. At the CTA, X-routes usually serve about 88% of the combined
stops.
15 Average taken from the data of the X-routes in the Ashland and Cicero corridors before and after the
implementation of the X-services.
16 As described earlier in the Chapter, the model is iterative since one of the outputs of the model is passenger
demand split which is also required for estimating running times. Therefore, once the demand split is estimated, the
running times can be re-estimated.
14
either Table 5-1 or Table 5-3 (depending on the number of doors, the enhanced boardings
elements, and the fare media).
Table 5-3 Passenger service times with multiple-channel passenger movement
Available
Door Channels
1
Default Passenger Service Time (s/p)
Boarding* Front Alighting Rear Alighting
2.1
3.3
2.5
2
1.5
1.8
1.2
3
1.1
1.5
0.9
4
0.9
1.1
0.7
6
0.6
0.7
0.5
Note: * Assumes no on-board fare payment required
Note: Increase boarding times by 20% when standees are present. For lowfloor buses, reduced boarding times by 20%, front alighting by 15%, and
rear alighting times by 25%
Source: TCRP Report 100
The change in traffic time depends on the level of segregation from general traffic. In the case of
a fully segregated right-of-way which does not allow right turns or interaction with traffic, the
traffic time can be reduced to zero. In the case of preferential right of way, the traffic time
reduction depends on the number of right turns allowed, level of traffic in the adjacent lane, and
the level of enforcement of the preferential lane. In this case about a 50% reduction in traffic
time can be assumed. When simple limited-stop services are implemented a no reduction in
traffic time should be expected.
The change in traffic signals depends on many variables such as the preemption treatment in the
corridor (Passive, Active, Real-Time, and Preemption). As discussed in Chapter 2, there is no
general agreement in the literature on how much travel time savings can be attributed to transit
signal priority. Furth17 acknowledges this difficulty and lack of literature on the prediction of
travel time reductions as a result of the implementation of TSP strategies. For assessing the
percentage reduction in time spent at traffic lights, Furth suggests using a range of:
0
10 to 20% traffic delay reduction for the typical timid priority applied in the US,
17 Interview
by author with Professor Peter Furth. Department of Civil and Environmental Engineering,
Northeastern University, 2010
"
PLUS 20 to 30% further traffic delay reduction from having a queue jump lane,
* PLUS 20% further traffic delay reduction from using intelligent and aggressive signal
priority tactics.
Origin-Destination (OD) Demand Matrix Estimation
5.5
The OD demand matrix is estimated from the boardings and alightings at each stop using the
methodology developed by Navick and Furth (1994) and also used by Schwarcz (2004). The
methodology consists of using the distance matrix as a seed matrix and then using the Iterative
Proportional Fitting (IPF) procedure.
Passenger demand assignment and estimation of demand changes
5.6
Once the OD matrix has been estimated, the model assigns the demand and estimates the demand
changes at the OD level. This process is carried out in four stages:
e
Calculate, at the OD pair level, the travel time split into access, egress, waiting, and invehicle times for each of the three strategies passengers can choose: waiting for the local
service, waiting for the limited-stop service, or taking the first service that comes.
" Assign passengers to one of these three strategies.
* Assign passengers to one of the two "physical" services (local or limited-stop) according
to the probability of choosing a strategy and the frequency share (the percentage of
limited-stop bus trips to the total local and limited-stop bus trips).
.
Take the changes in passenger travel time based on the chosen strategy and apply the
travel time elasticities to estimate ridership changes. If any BRT component is
implemented, apart from limited stops, the increases in ridership due to the branding
effect can be estimated with the methodology proposed by the TCRP Report 118 (2007)
and described in section 2.2 of this thesis.
The demand assignment process is explained in detail in the following sub-section.
5.6.1
Passenger travel time estimation
Each component of the passenger travel time (access, egress, waiting, and in-vehicle times)
should be computed at the OD pair level for the three strategies passengers can choose (waiting
for the local service, waiting for the limited-stop service, or taking the first service that comes) as
follows:
Total Access Time
Total access time (access and egress) is determined by multiplying the access and egress distance
by the average walking speed which is assumed here to be 250 ft/minute (TCRP Report 100,
2003). For the purpose of the model, the quantity of interest is the differential access time
between the local stop and the combined stop. If there is no information about the location of the
actual origins and destinations of the passengers using the service, it could be assumed, as
proposed by Schwarcz, that this differential walking time "is the distance from the nearest local
stop to the nearest combined stop at the origin and/or destination" (Schwarcz, 2004).
The total access time for local preferred customers is assumed to be zero, the distance from the
closest origin (and destination) local stop to the closest combined stop divided by the average
speed walking (250 ft/min) is the total access time for the limited preferred customers, and the
total access time for choice customers is given by Equation (5-2).
TA TFistBuS =ATLimited +F-ETLiited + (1 - F) - ETLimited
(5-2)
Where TA TFirstBus is the total access time for a choice customer
A TLimited is the access time for a limited preferred customer
ETLimnited is the egress time for a limited preferred customer
ETLocal is the egress time for a local preferred customer
F, is the limited frequency share
Waiting Time
Waiting times are estimated from the headways of the local and the limited-stop services,
separately and combined (for choice riders). It can be assumed1 8 that the headway Coefficient of
Variation (COV) is the same as prior to the implementation of the new service for the choice
customers, two thirds of the previous value for the local preferred customers, and one third of the
previous value for the limited preferred customers. The standard expression for expected waiting
times, derived by Welding (1957) and Osuna and Newell (1972), shown in Equation (5-3) is
used:
E(w) =
E(h)
-[1+COV]2
2
(5-3)
where:E(w) is the expected waiting time
E(h) is the average headway (for the local service, the limited-stop service, or the
combined services depending of the case)
CO V is the headway Coefficient of Variation
In-Vehicle Time
In-vehicle times are obtained from the estimated speed from the running times and the distance
between each OD pair. For choice customers, the in-vehicle time is assumed to be the weighted
average time of both the local and the limited-stop services as shown in Equation (5-4).
IVTFirstBus =
(1- F)- IVTChoiceLoc + F
- IVTLimitd
(5-4)
Where IVTFirstBus is the in-vehicle time for a choice customer
IVTChoiceLoc is the in-vehicle time for a local preferred customer from the closest
combined stop
IVTLimited is the in-vehicle time for a limited preferred customer
F, is the limited service frequency share
18This was found through the validation process of the Schwarcz model performed by the author in Route 9/X9 and
Route 54/X54 in the city of Chicago. See Chapter 3, section 0, and Figure 3-7 to see an illustration of the change in
COV in a corridor after the implementation of a limited-stop service
5.6.2
Market classification
As described in Chapter 4, passengers choose between three strategies (waiting for the local
service, waiting for the limited-stop service, or taking the first service that comes) rather than
between bus services.
In order to assign passengers to a market, it is necessary to estimate, for each OD pair, the
access, egress, waiting, and in-vehicle times for each possible strategy as described in the
previous section. With this information, it is possible to assign demand, between each OD pair,
using a path-size logit model that can be estimated using the methodology proposed in Chapter 4.
The probability that a passenger selects a specific strategy is given by Equation (5-5).
C
P(i Q
exp (V. + In PSI,)
1exp(V +In PSj)
(5-5)
jecrn
Where Vi is the disutility of alternative i
PSi is the Path Size correction factor for alternative i and individual n
Cin are the available alternatives for individual n
For the CTA Routes analyzed, as described in Chapter 4, the estimated disutilities (V) for each
alternative are given by Equations (5-6) through (5-8).
VLocal =3.45 VFirstBus
VLimited
0.408TATLocal -0.693WTLocal
=1.55 - 0.408TATFistBu
'408
Limited
-0.366VTLocal
-0.693WTFirstBus
0.693WTLimited
+ ln(PSLocl)
-0.3661VTFirstBus
0. 3 6 6 VT
ln(PSFirstBus)
+ ln(PSLimited)
(5-6)
(5-7)
(5-8)
where: VLocal, VFirstBus and VLimited are the disutilities for the wait-for-the-local-service, take-thefirst-service-that-comes, and wait-for-the-limited-stop-service alternatives respectively
TA TLocal, TA TFirstBus and TATLimited are the total access times for the wait-for-the-localservice, take-the service-that-comes, and wait-for-the-limited-stop-service alternatives
respectively
WTLocal, WTFirstBus and WTLimitel are the expected waiting times for wait-for-the-localservice, take-the-first-service-that-comes, and wait-for-the-limited-stop-service
alternatives respectively
IVTLocal, IVTFirstBus, and IVTLimited are the in-vehicle times for wait-for-the-local-service,
take-the-first-service-that-comes, and wait-for-the-limited-stop-service alternatives
respectively
PS is the Path-Size factor that corrects for the correlation between alternatives as
described in Chapter 4
Service and Stop Assignment
5.6.3
The stop assignment is also performed at the OD pair level. Based on the market classification,
the passengers that are local preferred are assigned to the local stop, and the limited preferred
and choice customers are assigned to the combined stop. The service assignment is also based on
the market classification: Passengers that are limited preferred are assigned to the limited-stop
service, passengers that are local preferred are assigned to the local service, and choice
passengers are assigned to the local or limited-stop service based on this frequency share. For
example, if the frequency share in a corridor is 3:2, 60% of the choice riders are assigned to the
limited-stop service and 40% to the local service.
5.6.4
Example of market classification and demand assignment
Figure 5-3 shows a hypothetical limited-stop service overlapped with a local service, the local
and the combined stops, the service frequencies, and access times between the local and the
combined stops for a trip between Stop 1 and Stop 11.
HeadwayLcd
= 15 min
HeadwayLimited: 10 mi
IVTLoca = 20 min
IVTChoiceLocal =
19 mi
IVTLimited
15 mi
IVThoice=
17 min
1
2
*
*
Combined Stop
Local Stop
Access timeLimited. 3 m
3
Figure 5-3 Market Classification Example
10
Egress timeLi
11
d:
2 mi
12
Other relevant information is:
* From the OD matrix estimation, it was established that prior to the implementation of the
limited-stop service, 20 trips occurred from Stop 1 to Stop 11.
*
The nearest combined stop to the origin is Stop 2 and the nearest combined stop to the
destination is Stop 10.
*
The walking time from Stop 1 to Stop 2 is 3 minutes and the walking time from Stop 10
to Stop 11 is 2 minutes.
* The existing local service headway is 6 minutes with a COV 2 of 0.6.
* The proposed headways are 10 minutes for the limited-stop service and 15 minutes for
the new local service. Therefore, the frequency share is 60%
*
The in-vehicle time for OD pair 1-11 was 21.5 minutes on the existing local service, 20
minutes on the new local service, 19 on the local choice19 service, and 15 minutes on the
limited-stop service.
" The trip distance is 3.5 miles.
Based on the above information, the results presented in Table 5-4 can be obtained from the
estimated logit model.
19The in-vehicle time for the local service from the nearest combined stop.
100
Table 5-4 Probabilities, access time, egress time, waiting time, and in-vehicle time
Access Time
E ress Time
Waiting Time
In-Vehicle Time
In (PS)
Total Time
Local
Limited
preferred
preferred
3.00
0.00
2.00
0.00
7.20
10.50
15.00
20.00
-0.27
-0.71
27.20
30.50
Choice
1.80
1.20
4.80
17.00
-0.77
24.80
P,
From the model, it can be estimated that for this hypothetical example 13% of the passengers
(2.5 passengers) are local preferred, 5% are limited preferred (1 passenger), and 82% (16.5
passengers) are choice.
The service and stop assignment is also performed at the OD pair level. From the 20 passengers
travelling between stops 1 and 11, 2.5 passengers are assigned to the local stop (stop 1), and 17.5
passengers to the combined stop (stop 2); and 9.1 passengers are assigned to the local service,
and 10.9 passengers are assigned to the limited-stop service.
5.6.5
Predicting changes in ridership
The changes in ridership are estimated in two steps. The first step predicts the ridership changes
due to changes in passengers' travel time. These ridership changes are estimated using travel
time elasticities for different trip lengths as explained and illustrated in Chapter 3. Table 5-5
shows the travel time elasticities estimated using data from several CTA corridors with limitedstop services. If the change in travel time is greater than 1.5 minutes for the 0-1 mile trips the use
of a -0.71 elasticity is recommended (i.e. the elasticity used for 1-2 mile trips) since the zero
elasticity for those trips is not appropriate if large changes in travel time are expected.
Table 5-5 Passenger Travel Time Elasticities in Chicago
Trip Length (mi)
0 to 1
1 to 2
2 to 3
3 to 4
More than 4
Elasticity
0.00
-0.71
-1.42
-2.14
-2.85
101
For each OD pair, the travel time is estimated before and after the implementation of the limitedstop service. The travel times after the implementation are taken from the strategy chosen by the
passengers and modeled in the market assignment.
Continuing with the previous hypothetical example, the estimated change in ridership for the 111 OD pair is 5.2% (1 extra trip). The elasticity used for this calculation is -2.1 -given that this a
3.5-mile trip- and the estimate used for change in travel time is -2.4% (26.320 minutes before the
implementation of the limited-stop service and 25.721 minutes after the implementation).
The second step in estimating the change in ridership accounts for the branding effect due to the
inclusion of various BRT elements. Table 5-6 shows an example of how to estimate the ridership
increases due to branding in the case of the implementation of a full BRT service in a
corridor/route with the following characteristics:
*
Special segregated at-grade bus lane
*
Unique design shelters with passenger amenities
e
Unique vehicle design
*
Frequency of 10 min or less
* Off-vehicle fare collection
e
20
Passenger information at stops
Total Time in the Old Local = 0 (access and egress time) + 3*(1+.6) (waiting time) + 21.5 (in-vehicle time)
2 Total Time After the implementation = 30.5 * 0.13 (local preferred) + 27.2 * 0.05 (limited preferred) + 24.8 *
0.82 (choice)
102
Table 5-6 Example of estimating the additional ridership due to Branding
COMPONENT
Running Ways (not additive)
Grade-separated busway (special right-of way)
At-grade busway (special)
Median arterial busway
All-day bus lanes (specially delineated)
Peak-hour bus lanes
Mixed traffic
Stations (additive)
Conventional shelter
Unique/attractively designed shelter
llumination
Telephones/security phones
Climate-controlled waiting area
Passenger amenities
Passenger services
Vehicles (additive)
Conventional vehicles
Uniquely designed vehicles (external)
Air conditioning
Wide multi-door configuration
Level boarding (low-floor or high platform)
Service Patterns (additive)
All-day service span
High-frequency service (10 min or less)
Clear, simple, service pattern
Off-vehicle fare collection
ITS Application (selective additive)
Passenger information at stops
Passenger information on vehicles
POINTS
PERCENTAGE
15%
20%
0
20%
1
15%
10%
0
0
5%
0%
0
0
0%
5%
15%
0
0%
1
2%
0
2%
3%
0
0
3%
1
3%
2%
0
15%
15%
0%
0
1
5%
0%
0
1
5%
5%
1
11%
15%
4%
0
1
4%
4%
1
3%
1
77
10%
1
7%
3%
0
BRT Branding (additive)
10%
7%
Vehicles and stations
Brochures/schedules
7%
3%
1
0
60%
15%
Subtotal
Branding
TOTAL
75%
For the given example, a total of 75 points are obtained. This value is multiplied by 0.25
resulting in an expected increase in ridership of 18.75% (with respect to the base riders that
choose the BRT service over the local service) due to the branding effect.
5.7
Model Outputs: Measures for Evaluating Limited-Stop Service Configurations
The measures to evaluate different configurations of limited-stop services overlapped with local
services are the outputs of the model and are described in the following sub-sections.
103
5.7.1
Market Share
The market share refers to the split between local preferred, limited preferred, and choice riders.
This indicator measures the customer's willingness to the take the limited-stop service (limited
preferred passengers). A service configuration with few customers in the local preferred and
most of the customers in the limited preferred is desirable. However, a configuration with few
limited preferred customers but a large percentage of choice riders can be also successful since
choice customers select the service based on the frequency share between both services.
5.7.2
Demand Split
The final service split is obtained based on the market assignment. This is an indicator of the
level of use of both services. Configurations with more riders on the limited-stop or BRT service
than on the local service are desired.
5.7.3
Average Passengers per Bus Trip
The average passengers per bus trip for the local and the limited-stop service is a measure of
productivity of both services and the evenness of load between them. This measure is obtained
based on the service frequencies and the demand. It is assumed that during the analyzed period
(usually the AM or PM peak) the demand is uniformly distributed over the number of buses
serving the corridor during that period.
5.7.4
Running Time Savings/Changes on Speeds
This indicator measures the benefit for the operator as running time savings (that can reduce fleet
and increase productivity). However, this indicator needs to be looked at carefully; for example,
a fast limited-stop (or BRT) service can be the result of an imbalance in demand between the
local (with crowded buses) and the limited-stop (or BRT) service.
104
5.7.5
Change in Average Passenger Travel Time
The change in average passenger travel time is obtained based on the demand for each OD pair,
the travel time before the implementation of the limited-stop service, and the estimated travel
time after the implementation (based on the market assignment). This is an indicator of the
benefit received by the passengers. Configurations with greater savings in average passenger
travel time are most desirable and configurations with increases in average passenger travel time
should not be implemented.
5.7.6
Change in Ridership
The change in ridership is obtained based on the changes in travel time at the OD pair level, the
OD Demand Matrix, the travel time elasticities, and the BRT branding elements. This indicator
measures the new riders switching from other modes and routes, as well as induced demand.
Configurations that attract more riders are the most desirable. However, it is important to
consider the capacity of the services at peak times, since in the case of a large increase of
demand, additional resources would need to be added to the corridor/route.
5.8
Summary
This chapter presented a methodology for modeling and evaluating limited-stop and BRT
services overlapped with local services. The methodology is an extension of that proposed by
Schwarcz (2004). The new methodology allows forecasting ridership changes due to the
implementation of these service configurations, assigns demand using a probabilistic (rather than
a deterministic) approach, and models running time changes when BRT elements are introduced.
105
6
MODEL APPLICATION
This chapter applies the methodology for modeling and evaluating limited-stop and BRT
services overlapped with local services, developed in Chapter 5, to the Chicago Avenue and
7 9
th
Street corridors in Chicago. These corridors were selected because they were part of the Chicago
BRT Pilot Program22 . Different limited-stop and BRT service scenarios overlapped with local
services are tested including variations in stop spacing, service frequencies, and different BRT
elements including: right-of-way, enhanced boarding, and Transit Signal Priority (TSP). The
outputs of the modeling process are used to evaluate the potential improvements of introducing
limited-stop and BRT services in the selected corridors and understanding how different BRT
elements, as well as stop spacing, and service frequencies, affect the corridors' performance.
The chapter is divided into two sections; the first section applies the model to the Chicago
Avenue corridor under different BRT and limited-stop service scenarios and summarizes the
findings for the corridor. The second section applies the model to the
7 9
th
Street corridor and
reports the results found.
6.1
Chicago Corridor Application
The following sub-sections describe the corridor, analyze its performance under different
limited-stop and BRT services scenarios, and present findings for the implementation of limitedstop and BRT services in the corridor.
6.1.1
Corridor Description
CTA Route 66 serves Chicago Avenue and is the baseline route for the scenarios tested. Route
66 is a 9-mile long route that runs from Austin, east along Chicago Avenue to Navy Pier and
crossing three CTA rail lines (Blue, Brown, and Red line) as shown in Figure 6-1.
Four corridors were selected including Jeffery, 7 9 1h, Chicago, and Halsted. See
httn://www.dot.state.iI.us/ooo/O8conference/Stehen%2OLittle BRT%20IDOT%209-26-08.ndf
2
106
Figure 6-1 CTA Route 66
Based on the spring 2008 data from the Automatic Vehicle Location (AVL) and the Automatic
Passenger Count (APC) systems, the following characteristics of Route 66 have been
established:
*
The route carries an average weekday ridership of 25,300 passengers making it the 3 rd
heaviest CTA bus route.
* During the morning peak period (from 6 to 9 am), the heaviest ridership is eastbound2 3
with 3,604 passengers.
e
The route has 74 bus stops with an average distance between stops of 0.12 miles (196
meters).
* A bus makes an average of 39 stops per trip.
* The average eastbound headway is 6 min. with an average COV 2 of 0.47.
*
23
The average speed is 8.31 mph (13.37 kph).
Unless otherwise noted this analysis focuses on the morning peak period and the eastbound direction.
107
* The averagepassenger travel time (including waiting and in-vehicle time) is 20 min.
e The average trip length is 2.1 miles (3.37 km). Figure 6-2 shows the trip length
distribution.
35%
30% 25%
20%
15%
10%
5% 0%
0
1
2
3
Trip Length (Miles)
Figure 6-2 Route 66 Trip Length Distribution
The cumulative demand for Route 66 eastbound during the morning peak period is shown in
Figure 6-3. In this figure, the boardings (or alightings) for each stop were summed to obtain the
total demand at each stop and then the stops were sorted by (decreasing) total demand and are
shown as the cumulative demand function. A straight line would mean equally distributed
demand at all stops along the corridor while a highly-curved line would mean highlyconcentrated demand. For Route 66, the top 20% of boarding stops serve about 56% of total
demand and the top 20% of alighting stops serve 64% of demand.
108
C
Cumulative Alightings
Cumulative Boardings
100%
100%
80% -
80%
60% -
60%
40% -
40%
20% -
20%
0%
0%
20%
40%
60%
80%
0% 40%
100%
20%
40%
60%
80%
100%
Percentage of total stops
Percentage of total stops
I
Figure 6-3 Cumulative AM Peak Eastbound demand for Route 66
Based on field observations (4 trips) carried out by the author during summer 2008 the running
time split for the AM peak eastbound was established. Additionally, the average running time
was found to be 65 minutes for the same period and direction using the data from the CTA
internal transitwebwebpage that publishes the average running times of different routes for
different seasons. Figure 6-4 shows the running time broken down into movement, traffic, dwell,
and traffic light components for the AM Peak eastbound.
I
Running Times Proportion
100%-
80%
60%
40%
Running Time Components
20%
16%
m Stop Lights
o Dwell
o Traffic
* Moving
Movement
Traffic
33.2
10.2
Dwell
13.9
Traffic Lights
Total (min)
7.7
65.0
20%-
0% !
Figure 6-4 Running Time Components for Route 66 in the AM Peak eastbound
109
6.1.2
Scenario 1: Conventional Limited-Stop
Alternative conventional limited-stop service configurations are examined in this sub-section,
distinguished by stop spacing and service frequency shares. Table 6-1 summarizes the
configurations tested including two stop spacings (0.33 and 0.40 miles) and five frequency
shares (local only, 1:1, 3:2, 2:1, and limited-stop only). The combined stops were selected based
on their level of demand (boardings and alightings) and the distance between stops (allowing no
more than three local stops between consecutive combined stops). The list of local and combined
stops for the different configurations is shown in Appendix B. All the tested combinations of
local and limited-stop service frequencies provide 10 bus trips per hour -the same as the current
local frequency. In other words, all the tested scenarios would require similar bus resources.
Table 6-1 summarizes the running times for different configurations. As shown, conventional
limited-stop service only affects the movement and the dwell times as a result of the savings
from skipping stops and the demand shift from the local to the limited-stop service. Change in
running times ranges from 8.1 minutes to 12.4 minutes savings for the limited-stop service, with
only small changes for the local service.
Table 6-1 Running times components in the Chicago Corridor Scenario 1: Conventional Limited-Stop Service
Sime
(min)
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
Change
Ine Change
Time
(mini
Change
Time
(min)
Chag
Chne
Time
ng 1 (min)
Change
Chng
Time
(min)
Change
Chng
Time
I(min)
Chne
Time
Chainge
(min)
33.2
10.2
13.9
7.7
65.0
0.0
0.0
0.0
0.0
0.0
33.2
10.2
15.4
7.7
66.5
0.0
0.0
1.5
0.0
1.5
33.2
10.2
14.0
7.7
65.1
0.0
0.0
0.1
0.0
0.1
33.2
10.2
12.9
7.7
64.0
0.0
0.0
-1.0
0.0
-1.0
33.2
10.2
15.9
7.7
67.0
0.0
0.0
2.0
0.0
2.0
33.2
10.2
14.6
7.7
65.7
0.0
0.0
0.7
0.0
0.7
33.2
10.2
12.9
7.7
64.0
0.0
0.0
-1.0
0.0
-1.0
-
-
-
-
25.1
-8.2
10.2
0.0
11.8
-2.1
7.7
0.0
54.8 -10.2
25.1
10.2
13.3
7.7
56.2
-8.2
0.0
-0.6
0.0
-8.8
25.1
10.2
14.0
7.7
57.0
-8.2
0.0
0.1
0.0
-8.1
22.1
10.2
11.1
7.7
51.1
-11.1
0.0
-2.8
0.0
-13.9
22.1
10.2
12.6
7.7
52.6
-11.1
0.0
-1.3
0.0
-12.4
22.1
10.2
13.8
7.7
53.8
-11.1
0.0
-0.1
0.0
-11.2
-
23.9
10.2
13.2
7.7
54.9
Change
hag
-
-9.3
0.0
-0.7
0.0
-10.1
Note*: For the limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the
other configurations it was assumed that the limited-stop service serves an average of 88% of the combined stops
(see section 5.4)
Note: The estimated Running Times do not include the effect of new demand
Running time savings in the corridor do not necessarily lead to increases in productivity,
increases in ridership, or reductions in average passenger travel time. These improvements also
110
depend on the trip length distribution, the demand distribution across local and combined stops,
and the trade-offs between increases in out-of-vehicle time and decreases in in-vehicle time.
Table 6-2 shows the six indicators for the seven configurations tested. The effect of changes in
total demand was not included in these indicators to avoid accounting for second order effects
such as increases in running times (given longer dwell times) that would lead to increases in
travel times, and changes in market share and demand split.
Table 6-2 Chicago Corridor Scenario 1: Conventional Limited-Stop Service
Market Shar
Local Preferred
Limited Preferred
-5.8%
20.3%
8.9%
11.7%
2.9%
16.0%
24.5%
7.0%
11.1%
13.3%
3.9%
17.3%
-
Choice
-
73.8%
79.5%
81.1%
68.5%
75.6%
78.8%
-
100%
-
57.2%
42.8%
40.6%
59.4%
30.5%
69.5%
58.7%
41.3%
41.4%
58.6%
30.7%
69.3%
100.0%
137.5
122.0
109.7
141.0
124.1
110.6
-
102.7
118.8
125.3
99.1
117.4
124.8
120.1
20.2
0.0%
20.8
3.2%
20.5
1.8%
20.3
0.5%
21.0
4.1%
20.6
2.5%
20.4
1.1%
20.4
1.0%
8.3
0.0%
-
8.1
-2.3%
9.9
18.7%
8.3
-0.1%
9.6
15.6%
8.4
1.6%
9.5
14.1%
8.1
-3.0%
10.6
27.1%
8.2
-1.1%
10.3
23.5%
8.4
1.6%
10.0
20.8%
9.8
18.3%
3602
0.0%
3563
-1.1%
3624
0.6%
3663
1.7%
3547
-1.5%
3624
0.6%
3670
1.9%
3684
2.3%
Demand Split
Local
Limited
Average Passengers per Trip
Local
120.1
Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
Local
Change
Limited-Stop
Change
Ridership
After
Change
-
Note: The Average Passengers per Trip, the Demand Split, and the speeds do not reflect any changes in total
ridership
There is no configuration that reduces the average passenger travel time compared with the base
scenario (20.2 minutes) in spite of the up to 12.4 minute reduction in running time for the
limited-stop service. The key reasons for this are the intrinsic route characteristics including the
short average trip length (2.1 miles), the cumulative demand function, and the short route length
(9 miles). Figure 6-5 shows, in detail, the changes in average passenger travel time for the
limited-stop only configuration. Short trips (0-2 miles) show increases in average passenger
travel time and longer trips (more than 2 miles) show decreases in average passenger travel time.
These travel time decreases cannot compensate for the increases in travel time in shorter trips
111
given the trip length distribution (see Figure 6-2).
2.00
1 000.00
E
F-
-1.00-2.00-
0)
-3.00-
-4.00 -5.00
Trip Length (Miles)
Figure 6-5 Change in travel time by trip length for the limited-stop service only configuration for Route 66
Table 6-2 also shows that greater frequency shares lead to lower average travel times and to
larger ridership increases (between 1%and 2%). Moreover, the 0.33 mile limited-stop
configurations perform slightly better than the 0.4 mile spacing since they lead to shorter average
travel times.
The best service design is a configuration with a 2:1 frequency share since it is the one that has
the shortest average travel times, the highest percentage of limited preferred riders, the highest
ridership with around 2% new riders, and the highest limited-stop service productivity.
Configurations with a frequency share of 1:1 show the poorest performance in terms of demand
split, the greatest disparity in productivity (and evenness of load) between the local and the
limited-stop service, the lowest percentage of limited preferred riders, and highest average travel
times. Note that the higher speeds for the limited-stop services in the 1:1 configurations result
from low demand for the limited-stop buses (which leads to shorter dwell times).
The limited-stop only service configuration shows the largest ridership increases (2.3%) given
the travel time savings on longer trips (see Figure 6-5). This configuration can reduce resources
needed in the corridor given the speed increases (from 8.31 to 9.83 mph); however, this
configuration would increase average passenger travel time by 1% and it likely to be highly
controversial since it will negatively affect people with mobility constraints. Recent attempts to
increase general stop spacing in Chicago have been met with strong political opposition.
112
6.1.3
Scenario 2: Moderate BRT enhancements
Different configurations of moderate BRT services are modeled in this sub-section. The
configurations tested vary service frequencies, right-of-way, enhanced boarding elements, and
Transit Signal Priority (TSP). The combined stops selected are the same as in Scenario 1 (see
Appendix B). All the scenario 2 configurations have a total frequency of 10 buses per hour (the
same as the existing local service frequency).
The BRT elements tested include a preferential (rather than a segregated) lane, buses with two
doors and fare payment prior to boarding the bus, and a 35% reduction in traffic light delays due
to TSP (see 2.4.2). Table 6-3 shows the tested configurations which include three frequency
shares (3:2, 2:1, limited-stop only) and three combinations of BRT elements (preferential lane
only; enhanced boarding and TSP only; and preferential lane, enhanced boarding and TSP).
Table 6-3 shows BRT effects on running times. Decreases in running times range from 13.8 to
24.7 minutes for the limited-stop service and from 0.8 to 9.8 minutes for the local service.
Table 6-3 Running times components in the Chicago Corridor Scenario 2: Moderate BRT Services
__
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
_
_
Time
Time Change i ime
Change
(min
(min
(min)___
0.0
0.0
0.0
0.0
0.0
33.2
10.2
13.9
7.7
65.0
-
-
-
-
-
-
Change
men Change
(min)
u
in
Change """
(min)
Change*
Change
Change
30.0
5.1
14.6
7.7
57.3
-3.2
-5.1
1.5
0.0
-7.7
30.0
5.1
13.1
7.7
55.9
-3.2
-5.1
0.1
0.0
-9.1
33.2
10.2
13.1
7.7
64.2
0.0
0.0
0.0
0.0
-0.8
33.2
10.2
10.7
7.7
61.8
0.0
0.0
-2.4
0.0
-3.2
30.0
5.1
13.7
7.7
56.4
-3.2
-5.1
0.6
0.0
-8.6
30.0
5.1
12.4
7.7
55.2
-3.2
-5.1
-0.7
0.0
-9.8
24.7
5.1
12.9
7.7
50.4
-8.5
-5.1
-0.2
0.0
-14.7
24.7
5.1
13.7
7.7
51.2
-8.5
-5.1
0.6
0.0
-13.8
25.1
10.2
5.7
5.0
45.9
-8.2
0.0
-7.4
-2.7
-19.1
25.1
10.2
6.2
5.0
46.5
-8.2
0.0
-6.8
-2.7
-18.5
24.7
5.1
5.5
5.0
40.3
-8.5
-5.1
-7.5
-2.7
-24.7
24.7
5.1
5.7
5.0
40.5
-8.5
-5.1
-7.3
-2.7
-24.5
-
25.7
5.1
5.6
5.0
41.4
-
-7.5
-5.1
-7.5
-2.7
-23.6
Note*: For limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the
other configurations it was assumed that the limited-stop service serves an average of 88% of the local stops (see
section 5.4)
Note: The estimated Running Times do not include the effect of new demand
Table 6-4 shows the six indicators for the seven tested configurations. All configurations show
better performance (higher speeds, shorter travel times, ridership increases, etc.) than the base
113
scenario. The effect of changes in total demand was not included in these indicators to avoid
accounting for second order effects such as increases in running times (given longer dwell times)
that would lead to increases in travel times, and changes in market share and demand split.
Table 6-4 Chicago Corridor Scenario 2: Moderate BRT Services
Local Preferred
Limited Preferred
Choice
Demand Split
Local
Limited
Average Passengers per Trip
Local
Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
Local
Change
Limited-Stop
Change
Ridership
After without branding
Change
After including branding
Total Change
9.9%
10.4%
79.6%
3.6%
15.1%
81.3%
6.9%
16.6%
76.5%
2.4%
20.8%
76.8%
7.8%
14.8%
77.4%
2.5%
20.0%
77.5%
100%
-
41.8%
58.2%
31.2%
68.8%
37.5%
62.5%
28.5%
71.5%
38.7%
61.3%
28.9%
71.1%
120.1
-
125.4
116.5
112.6
123.8
112.6
125.1
102.6
128.8
116.3
122.6
104.1
128.1
120.1
20.2
0.0%
19.2
-4.9%
18.9
-5.9%
18.8
-6.7%
18.3
-9.1%
17.5
-12.9%
17.1
-15.2%
15.2
-24.6%
8.3
0.0%
9.4
13.4%
10.7
29.0%
9.7
16.2%
10.6
27.0%
8.4
1.2%
11.8
41.5%
8.7
5.2%
11.6
39.7%
9.6
15.2%
13.4
61.1%
9.8
17.8%
13.3
60.3%
13.0
56.9%
3900
8%
4136
14.8%
3935
9%
4183
16.1%
3981
11%
4234
17.5%
4067
13%
4357
21.0%
4240
18%
4653
29.2%
4322
20%
4803
33.3%
4662
29%
5455
51.4%
-
3602
0%
-
-
100%
-
-
Note: The high demand increase due to ridership in the limited-stop only configuration is explained by the fact that
the methodology used multiplies the branding factor times the base ridership of the limited-stop service. For the
limited-stop only case this is 100% of the demand prior the implementation of the new service.
Note: The Average Passengers per Trip, the Demand Split, and the speeds do not reflect the change in ridership
Table 6-4 also shows that greater frequency shares again lead to shorter travel times, higher
percentage of limited preferred riders, higher percentage of limited preferred riders, greater
productivity for the limited-stop service, and greater increases in ridership.
In the case of Route 66, the implementation of enhanced boarding and TSP is slightly more
effective in reducing travel times and attracting demand than the implementation of preferential
treatment. However, in the first case, the local service speed does not show improvement since it
114
does not benefit from the enhanced boarding or the TSP while in the second case, the local
service also benefits from preferential lanes.
The BRT only scenario shows the best results in terms of travel time reductions and new riders.
Figure 6-6 shows in detail the changes in average passenger travel time for different trip lengths.
In contrast with the Scenario 1 limited-stop only service configuration, the BRT only
configuration provides travel time savings for passengers across all trip lengths; however, the
implementation of BRT only strategy can be controversial since it will force all the passengers
living in the vicinity of a local stop to walk to their closest combined stop.
0
0
-
5
E
-10
C
f
-15-
-20
Trip Length (Miles)
Figure 6-6 Change in travel time by trip length for the moderate BRT only service configuration
Branding is a major component of the predicted ridership gains; however, the ridership forecast
due to branding has a high degree of uncertainty that is the result of assuming the
implementation of all BRT elements that account for the "branding effect" (and following the
methodology) described in the TCRP Report 118 (2007). These ridership gains are not always
achievable and/or may be constrained by the available bus capacity.
Extra capacity may be needed with the implementation of BRT services. Table 6-5 shows the
effect of the new ridership on demand split and average passengers per trip assuming no
additional effect on running times. To assign the extra demand to both services, it was assumed
that new riders attracted by increases in level of service would have the same market split as
current riders and that new riders attracted by branding would take the BRT service. The results
show that both the local and BRT services can have increased demand (change in passengers per
115
trip) with respect to the all-local base scenario of up to 14% and 51% respectively.
Table 6-5 Chicago Corridor Scenario 2 including the effect of new demand: Moderate BRT Services
uemana
Local
Limited
Demand Split
Local
Limited
Average Passengers per Trip
Local
Change (Respect to all local)
Limited-Stop
Change (Respect to all local)
1629
2507
1227
2956
1493
2741
1155
3203
1642
3011
1243
3560
54551
100%
-
39%
61%
29%
71%
35%
65%
26%
74%
35%
65%
26%
74%
-
120
136
13%
139
16%
123
2%
148
23%
124
4%
152
27%
115
-4%
160
33%
137
14%
167
39%
124
4%
178
48%
-
-
100%
-
182
51%
To estimate if extra capacity is need, it is necessary to examine the maximum load point of the
route and observe if the extra demand for each service can be served by the same number of
buses. Figure 6-7 shows that currently Route 66 has a maximum load of 52 passengers per bus. If
demand on the local service is increased by 14%, the maximum load point using the same
number of buses would be 60 people. Therefore, the capacity provided is not enough given a bus
capacity of 55 passengers.
Max Load Point =52
60
50
40
m 30
20
10
0
0
10000
20000
30000
40000
50000
Distance (Miles)
Figure 6-7 Route 66 Load Profile
116
The new demand for the local service (in the case of a 14% increase), would require one extra
local bus per hour. This number is computed as: 14% extra riders per bus trip, under the 15minute headway, are equal to 60 passengers (52 x 1.14) per bus, this means that 5 more seats are
needed per bus. Therefore, the extra demand is equal to 20 passengers per hour (5 x 60/15) that
could be served with one extra 55-passenger bus.
The 51 % demand increase for BRT buses would mean that the maximum load point would be
79 passengers. However, in this case this demand can be served with the proposed headway if
articulated buses are used (since they have a 90-passenger capacity).
6.1.4
Scenario 3: Full BRT
Different full BRT service configurations are modeled in this sub-section. As in Scenario 2, four
different elements are tested: service frequencies, right-of-way segregation, enhanced boarding
elements, and Transit Signal Priority (TSP). The combined stops selected are the same as in
Scenario 2 (see Appendix B). All the scenario 3 combinations have a total frequency of 10 buses
per hour (the same of the prior local frequency). The BRT elements tested include a completely
segregated lane, buses with three doors with off-bus fare payment, and a 35% reduction on
traffic light delays due to TSP (see section 2.4.2). The configurations tested include two
frequency shares (3:2, and 2:1) and three BRT elements (segregated lane only; enhanced
boarding and TSP only; and preferential lane, enhances boarding and TSP).
Table 6-6 shows full BRT effects on all running time components. Decreases in running times
range from 20.1 minutes to 34.8 minutes for the limited-stop service and from 0.8 minutes to
21.5 minutes for the local service.
117
Table 6-6 Running times components in the Chicago Corridor Scenario 3: Full BRT Services
Headway(in)-Umed/Local
t-lt-
w1V
-
Change T
__
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
__
_
_
Change
(min)
(min)__
s
S
-1al
.
(min)
TWIMDM
W
Change
Change
201MMC
(min)
Change
(min)
Change
T
(min)
Change
mn
33.2
10.2
13.9
7.7
65.0
0.0
0.0
0.0
0.0
0.0
24.5
0.0
14.6
7.7
46.8
-8.7
-10.2
0.7
0.0
-18.2
25.5
0.0
13.3
4.7
43.5
-7.7
-10.2
-0.6
-3.0
-21.5
33.2
10.2
13.1
7.7
64.2
0.0
0.0
0.0
0.0
-0.8
33.2
10.2
12.0
7.7
63.2
0.0
0.0
-1.0
0.0
-1.8
24.5
0.0
13.7
7.7
45.9
-8.7
-10.2
0.6
0.0
-19.1
24.5
0.0
12.4
7.7
44.6
-8.7
-10.2
-0.7
0.0
-20.4
-
-
-
-
-
20.8
0.0
12.7
7.7
41.1
-12.5
-10.2
-1.2
0.0
-23.9
20.8
0.0
13.8
7.7
42.3
-12.5
-10.2
-0.1
0.0
-22.7
25.1
10.2
4.5
5.0
44.8
-8.2
0.0
-8.6
-2.7
-20.2
25.1
10.2
4.6
5.0
44.9
-8.2
0.0
-8.5
-2.7
-20.1
20.8
0.0
4.4
5.0
30.2
-12.5
-10.2
-8.7
-2.7
-34.8
20.8
0.0
4.6
5.0
30.3
-12.5
-10.2
-8.5
-2.7
-34.7
21.8
0.0
4.5
5.0
31.3
Change*
-
-11.4
-10.2
-8.6
-2.7
-33.7
Note*: For limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the other
configurations it was assumed that the limited-stop service serves an average of 88% of the local stops (see section
5.4)
Note: The estimated Running Times do not include the effect of new demand
Table 6-7 shows the six indicators for the seven tested configurations. All configurations show a
better performance (higher speeds, shorter travel times, greater ridership increases, etc.) than the
base scenario and the Moderate BRT scenario. The effect of changes in total demand was not
included in the indicators to avoid accounting for second order effects such as increase in
running times (given longer dwell times) that would lead to increases on travel times, and
changes in the market share and demand split.
Table 6-7 shows again that higher frequency shares lead to greater reductions in travel time,
higher percentage of limited preferred riders, greater productivity for the limited-stop service,
and greater increases in ridership. The lower speeds on the limited-stop service configurations
with high frequency shares are the result of high demand producing longer dwell times.
118
Table 6-7 Chicago Corridor Scenario 3: Full BRT Services
Market Share
Local Preferred
Limited Preferred
10.5%
10.3%
3.8%
14.9%
81.2%
6.7%
17.3%
76.0%
2.1%
22.6%
75.3%
8.1%
15.3%
76.7%
2.6%
20.6%
76.8%
100%
-
42.2%
57.8%
31.5%
68.5%
37.1%
62.9%
27.7%
72.3%
38.7%
61.3%
28.7%
71.3%
120.1
-
126.5
115.8
113.3
123.4
111.4
125.9
99.9
130.2
116.2
16.
122.6
103.5
105
128.4
120.1
20.2
0.0%
16.8
-16.7%
16.6
-17.5%
18.6
-7.7%
18.1
-10.2%
15.0
-25.4%
14.5
-27.9%
12.6
-37.6%
8.3
0.0%
-
11.5
38.9%
13.1
58.1%
11.9
43.0%
12.8
53.8%
8.4
1.2%
12.1
45.1%
8.6
2.9%
12.0
44.8%
11.8
41.6%
17.9
115.5%
12.1
45.6%
17.8
114.4%
-
17.2
107.0%
4393
22%
4659
29%
4415
23%
4730
31%
4022
12%
4322
20%
4115
14%
4460
24%
4763
32%
5249
46%
4852
35%
5416
50%
5197
44%
5990
66%
.
Choice.
Service Split
Local
Limited
Average Passengers per Trip
Local
Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
Local
Change
Limited-Stop
Change
Ridership
After without branding
Change
After including branding
Total Change
-
3602
0%
-
-
100%
-
by the fact that
Note: The high demand increase due to ridership in the limited-stop only configuration is explained
For the
service.
limited-stop
the
of
ridership
base
the
times
factor
branding
the
multiplies
used
the methodology
service.
new
the
of
implementation
the
prior
demand
limited-stop only case this is 100% of the
Note: The Average Passengers per Trip, the Demand Split, and the speeds do not reflect any change in ridership
For Route 66, the implementation of preferential treatment alone is more effective in reducing
the travel times and attracting demand that implementing only enhanced boarding and TSP
alone. The implementation of the exclusive lane also benefits the local service whereas other
improvements only affect the BRT service.
The BRT only scenario shows the best performance (travel time reductions, and new attracted
riders). Figure 6-8 shows in detail the changes in average passenger travel time for different trip
lengths. As for the full BRT configuration of Scenario 2, this configuration provides travel time
savings for passengers across all trip lengths. Full BRT configurations provide the largest travel
savings (37.6%). However, the implementation of this configuration will affect all passengers
119
,~
living in the vicinity of a local stop and the passengers with walking disabilities.
0
-5
E
-10
-15
-20
Trip Length (miles)
Figure 6-8 Change in travel time by trip length for the full BRT only configuration
Once more, branding is a major component of the predicted ridership gains; however, the
ridership forecast due to branding has a high degree of uncertainty that is the result of assuming
the implementation of all BRT elements that account for the "branding effect" (and following the
methodology) described in the TCRP Report 118. These ridership gains are not always
achievable and/or may be constrained by the available bus capacity.
Extra capacity may be needed with the implementation of BRT services. Table 6-8 shows the
effect of the new ridership on demand split and average passengers per trip assuming no effect
on running times. To assign the extra demand to both services, it was assumed that new riders
attracted by increases in level of service would have the same market split as current riders and
that new riders attracted by branding would take the BRT service. The results show that the local
and BRT services can have demand increases (change in passengers per trip) with respect to the
all-local base scenario of up to 28% and 68% respectively.
120
Table 6-8 Chicago Corridor Scenario 3 including the effect of new demand: Full BRT Services
1852
1383
2807
3348
1492
2830
1138
3323
1845
3404
1385
4032
5990
100%
-
40%
60%
29%
71%
35%
65%
26%
74%
35%
65%
26%
74%
100%
120
154
28%
156
30%
138
15%
167
39%
124
4%
157
31%
114
-5%
166
38%
154
28%
189
57%
138
15%
202
68%
Local
I
A
d
i
Demad Split
Local
Limited
Average Passengers per Trip
Local
Change (Respect to all local)
Limited-Stop
Change (Respect to all local)
-
-
200
66%
To estimate if extra capacity is need, it is necessary to examine the maximum load point of the
route and observe if the extra demand for each service can be served by the same number of
buses. As shown in Figure 6-7, currently Route 66 has a maximum load point of 52 passengers
per bus. If demand on the local service is increased by 28%, the maximum load point using the
same number of buses would be 67 people. Therefore, the capacity provided is not enough given
a bus capacity of 55 passengers.
The new demand for the local (in the case of a 28% increase), would require one extra local bus
per hour. This number is computed as: 28% extra riders per bus trip, under a 15-minute headway,
are to equal to 67 passengers (52 x 1.28) per bus, this means that 12 more seats are needed per
bus. Therefore, the extra demand is equal to 48 passengers per hour (12 x 60/15) that can be
served with one extra 55-passenger bus.
The 68% extra demand for BRT buses would mean that the maximum load point would be 88
passengers. In this case this demand can be served with the proposed headways if articulated
buses are used (since they have a 90-passenger capacity).
6.1.5
Summary of Chicago Avenue Corridor Findings
The implementation of conventional limited-stop service overlapped with local service is not
121
recommended on Chicago Avenue since it will increase average travel times. This poor
performance is the result of the route characteristics, specifically the short route length (9 miles)
and average trip length (2.1 miles), and the cumulative demand function.
In contrast, implementing a BRT service would improve the corridor performance. If a BRT
system is implemented, right-of-way preferential treatment is key to achieving better
performance as it can increase the movement speeds and reduce the traffic delay time. The
recommended service frequency share is 2:1. A full BRT system could reduce travel time by up
to 28% while a Moderate BRT system can reduce it by up to 18%. If service speeds and travel
time need to be improved further, an aggressive TSP scheme could be implemented (reducing
the traffic light delay time by more than 35%).If BRT configurations are implemented, it is
strongly recommended to use articulated buses for the BRT services to provide the extra capacity
that might be needed. In addition, one additional conventional local bus per hour might be
needed as the result of demand increase.
The analysis show that BRT-only configurations provide the best performance in terms of travel
time reductions and ridership increases; however, implementing such a configuration will be
controversial since it will be very difficult for people with walking disabilities and will
significantly increase their access and egress times. Recent attempts to increase general stop
spacing in Chicago have met with strong political opposition.
6.2
Application:
7 9
th
Street Corridor
This section describes the
7 9
th
street corridor, analyzes its performance under different limited-
stop and BRT service scenarios, and present findings for the implementation of limited-stop and
BRT services in the corridor.
6.2.1
Corridor Description
CTA Route 79 serves
7 9 th Corridor
and is the baseline route for the different scenarios tested.
Route 79 is a 12.2-mile long route that runs from the vicinity of Cicero, east along 79th corridor
to the Lakefront. The service crosses the Red Line as shown in Figure 6-9.
122
Figure 6-9 CTA Route 79
Based on the CTA spring 2008 data from the Automatic Vehicle Location (AVL) and the
Automatic Passenger Count (APC) systems, the following characteristics of Route 79 have been
established:
" The route carries an average weekday ridership of 35,642 passengers making it the
heaviest ridership CTA bus route.
" During the morning peak period (from 6 to 9 am), the heaviest ridership is westbound
with 3,892 passengers
*
24
The route has 101 stops with an average distance between stops of 0.12 miles (196
meters).
* A bus makes an average of 35 stops per trip.
*
The average headway is 4 min. with an average COV 2 of 0.75.
" The average speed is 11.49 mph (18.49 kph).
* The average passenger travel time (including waiting and in-vehicle time) is 15.6 min.
24
Unless otherwise noted this analysis focuses on the morning peak period and the westbound direction.
123
*
The average trip length is 2.0 miles (3.37 km). Figure 6-10 shows the trip length
distribution.
35%
30%
25%
20%
15%
10%
5%
0%
0
1
2
3
Trip Length (Miles)
Figure 6-10 Route 79 Trip Length Distribution
The cumulative demand for Route 79 westbound during the morning peak period is shown in
Figure 6-11. The top 20% of boarding stops serve about 68% of total demand and the top 20% of
alighting stops serve 83% of demand. Note that this is a significantly higher concentration than
for Route 66.
Cumulative Boardings
Cummulative Alightings
100%
100%
80%
80%
60%
60%
40%
40%
20%
20%
0%
20%
40%
60%
80%
100%
0% 40%
Percentage of total stops
20%
40%
60%
80%
100%
Percentage of total stops
Figure 6-11 Cumulative AM Peak Westbound demand for Route 79
124
Based on field observations (3 trips) carried out by the author during summer 2008 the running
time split for the AM peak westbound was established. The average running time was found to
be 63.7 minutes for the same period and direction using the data from the CTA internal
transitweb webpage that publishes the average running time of all CTA routes for different
seasons. Figure 6-12 shows the running time broken down into movement, traffic, dwell, and
stop lights components for the AM Peak westbound.
Running times Proportion
100%
19%
80%
Running Time Components
-
Movement
60%
40%
20%
Stop Lights
13%_.
ODwell
0 Traffic
* Moving
-
Traffic
Dwell
Lights
Total (min)
37.5
8.6
10.4
7.2
63.7
-
0%
Figure 6-12 Running Time Components for Route 79 in the AM Peak westbound
6.2.2
Scenario 1: Conventional Limited-Stop
Alternative conventional limited-stop service configurations are examined in this sub-section,
distinguished by stop spacing and service frequencies. Table 6-9 summarizes the changes in
running times of the configurations tested including two stop spacings (0.37 and 0.42 miles) and
four frequency shares (1:1, 3:2, 2:1, and limited-stop only). The combined stops were selected
based on their level of demand (boardings and alightings) and the distance between stops
(allowing no more than three local stops between consecutive combined stops). The list of local
and combined stops for the different configurations is shown in Appendix B. All the tested
combinations of local and limited-stop service frequencies provide 15 bus trips per hour -the
same as the current local frequency on Route 79. In other words, all the tested scenarios would
125
require similar bus resources.
As shown in Table 6-9, conventional limited-stop service affects the movement and dwell times
as a result of the savings from skipping stops and the demand shift from the local to the limitedstop service. Changes in running time ranges from a decrease in 2.5 minutes to a decrease in 8.9
minutes for the limited-stop service, and from an increase in 1.6 minute to a 0.1 minute decrease
for the local service.
Table 6-9 Running times components in the 79th Street Corridor Scenario 1: Conventional Limited-Stop
Service
_______________________
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
(min)
Change
a
(min)
0.0
0.0
0.0
0.0
0.0
37.5
8.6
12.0
7.2
65.3
0.0 37.51
0.0
8.55
1.6 11.26
0.0
7.24
1.6
64.6
32.8
8.6
8.4
7.2
57.1
-4.7 32.84
0.0
8.55
-2.0
9.42
0.0
7.24
-6.6
58.1
37.5
8.6
10.4
7.2
63.7
-
-
Change
Time
(min)
Change
Time Change
(min)
Time
(min)
0.0 37.51
0.0
8.55
0.9 10.31
0.0
7.24
0.9
63.6
0.0
0.0
-0.1
0.0
-0.1
-4.7
0.0
-1.0
0.0
-5.7
-4.7 31.09
0.0
8.55
-0.4
7.95
0.0
7.24
-5.0
54.8
32.84
8.55
10.04
7.24
58.7
37.51
8.55
12.3
7.24
65.6
Change
(min)
0.0 37.51
0.0
8.55
1.9
11.7
0.0
7.24
1.9
65.0
-6.4
0.0
-2.5
0.0
-8.9
31.09
8.55
8.98
7.24
55.9
Change
(min)
Change
(min)
Change
hag
-
0.0
0.0
1.3
0.0
1.3
37.51
8.55
10.57
7.24
63.9
0.0
0.0
0.2
0,0
0.2
-
-6.4
0.0
-1.4
0.0
-7.8
31.09
8.55
9.76
7.24
56.6
-6.4
0.0
-0.6
0.0
-7.1
35.17
8.55
10.2
7.24
61.2
-2.3
0.0
-0.2
0.0
-2.5
Note*: For the limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the
other configurations it was assumed that the limited-stop service serve an average of 88% of the local stops (see
section 5.4)
Note: The estimated Running Times do not include the effect of new demand
Running time savings in the corridor do not necessarily lead to increases in productivity,
increases in ridership, or reductions in average passenger travel time. These improvements also
depend on the trip length distribution, the demand distribution across local and combined stops,
and the trade-offs between increases in out-of-vehicle time and decreases in in-vehicle time, etc.
Table 6-10 shows the six indicators for the seven tested configurations. The effect of changes in
total demand was not included in the indicators to avoid accounting for second order effects such
as increases in running times (given longer dwell times) that would lead to increases on travel
times, and changes in the market share and demand split.
126
Table 6-10
7 9
th
Street Corridor Scenario 1: Conventional Limited-Stop Service
E
-~~
4nlI.,
Market Share
Local Preferred
Limited Preferred
D enIIIIU d S
i~
25.6%
7.8%
666%
.
16.0%
14.2%
69.8%
-
8.6%
19.3%
72.1%
30.6%
8.1%
61.3%
19.3%
14.6%
66.1%
10.5%
19.7%
69.8%
100%
-
58.9%
41.1%
43.9%
56.1%
33.1%
66.9%
61.2%
38.8%
45.7%
54.3%
34.3%
65.7%
86.1
101.3
70.8
94.5
80.5
85.4
86.4
105.4
66.7
98.3
77.9
88.5
84.8
-
15.6
0.0%
16.3
4.7%
16.3
4.7%
16.1
3.7%
16.5
6.2%
16.5
6.2%
16.4
5.3%
15.9
2.0%
11.5
0.0%
-
11.2
-2.3%
12.8
11.7%
11.3
-1.3%
12.6
9.7%
11.7
2.1%
12.5
8.6%
11.2
-2.9%
13.4
16.2%
11.3
-2.0%
13.1
14.0%
11.5
-0.3%
12.9
12.4%
-
3872
0.0%
3738
-3.5%
3760
-2.9%
3794
-2.0%
3721
-3.9%
3742
-3.4%
3782
-2.3%
3834
-1.0%
ItA
PI
Demand Split
Local
Limited
Average Passengers per Trip
Local
Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
Local
Change
Limited-Stop
Change
Ridership
After
Change
~
100%
86.1
-
12.0
4.2%
,
Note: The Average Passengers per Trip, the Service Split, and the speeds do not include the change in ridership
There is no configuration that reduces the average passenger travel time with respect to the base
scenario (15.6 minutes). The key reasons for this are the intrinsic route characteristics including
the short average trip length (2.0 miles), the high speed of the existing local service (11.49 mph),
and the small average number of stops serviced per trip (35 of 101 stops). Figure 6-13 shows the
changes in travel time for different trip lengths of the limited-stop only service configuration,
there are traVel time increases for trips shorter than 3 miles and modest increases for longer trips.
2
0
E
-
-
-2
0
1
2
3
10)
.c O-4-5
Trip Length (Miles)
Figure 6-13 Change in travel time by trip length for the limited-stop only service configuration for Route 66
127
Table 6-10 shows that greater frequency shares lead to lower average travel times -as in Route
66- but there is no limited-stop configuration that show travel time reductions or increases in
ridership with respect to the base scenario. Therefore, a configuration with limited-stop service
overlapped with a local service does not seem appropriate for the 79th Street corridor.
A limited-stop only service configuration does not improve the corridor performance either;
although it does show the "best" performance of all proposed configurations with an increase in
service speed from 11.5 mph to 12 mph.
6.2.3
Scenario 2: Moderate BRT enhancements
Different configurations of moderate BRT services are analyzed in this sub-section. The
configurations tested vary service frequencies, right-of-way, enhanced boarding elements, and
Transit Signal Priority (TSP). The combined stops selected are the same as in the previous subsection (see Appendix B). As in scenario 1, all the scenario 2 combinations have combined
frequency of 15 buses per hour (the same as the prior local frequency).
The BRT elements tested include a preferential (rather than a segregated) lane, buses with two
doors and off-vehicle fare payment, and a 35% reduction in traffic light delays due to TSP (see
section 2.4.2). Table 6-11 shows the tested configurations which include two frequency shares
(3:2, and 2:1) and three combinations of BRT elements (preferential lane only; enhanced
boarding and TSP only; and preferential lane, enhanced boarding and TSP).
Table 6-11 shows BRT service effects on running times components. Decreases in running times
range from 13.1 to 23.2 minutes for the limited-stop service; and the change in running times for
the local service ranges from a 0.4 minute increase to a 7.8 minute decrease.
128
Table 6-11 Running times components in the
Time
(min)
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
Change
Time
(mini
Change
7 9 th
Street Corridor Scenario 2: Moderate BRT Services
I(min)
Change
(m
Change
(m
Change
(mini
Change
(mini
Change
(min)
37.5
8.6
10.4
7.2
63.7
0.0
0.0
0.0
0.0
0.0
34.9
4.3
11.3
7.2
57.6
-2.7
-4.3
0.9
0.0
-6.1
34.9
4.3
10.1
7.2
56.4
-2.7
-4.3
-0.4
0.0
-7.3
37.5
8.6
10.8
7.2
64.1
0.0
0.0
0.4
0.0
0.4
37.5
8.6
9.8
7.2
63.1
0.0
0.0
-0.6
0.0
-0.6
34.9
4.3
10.4
7.2
56.8
-2.7
-4.3
0.0
0.0
-6.9
34.9
4.3
9.5
7.2
55.9
-2.7
-4.3
-0.9
0.0
-7.8
-
-
27.2
4.3
9.4
7.2
48.1
-10.4
-4.3
-1.0
0.0
-15.6
27.2
4.3
10.2
7.2
48.9
-10.4
-4.3
-0.2
0.0
-14.9
32.8
8.6
4.3
4.7
50.4
-4.7
0.0
-6.1
-2.5
-13.3
32.8
8.6
4.5
4.7
50.6
-4.7
0.0
-5.9
-2.5
-13.1
27.2
4.3
4.4
4.7
40.5
-10.4
-4.3
-6.0
-2.5
-23.2
27.2
4.3
4.5
4.7
40.7
-10.4
-4.3
-5.9
-2.5
-23.0
Change
I
29.5
4.3
4.4
4.7
42.9
-8.0
-4.3
-6.0
-2.5
-20.8
Note*: For the limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the
other configurations it was assumed that the limited-stop service serves an average of 88% of the local stops (see
section 5.4)
Note: The estimated Running Times do not include the effect of new demand
Table 6-12 shows the six indicators for the seven tested configurations. All configurations show
better performance (higher speeds, shorter travel times, ridership increases, etc.) than the base
scenario. The effect of changes in total demand was not included in the indicators to avoid
accounting second order effects such as increases in running times (given longer dwell times)
that would lead to increases in travel times, and changes in the market share and demand split.
The results presented in Table 6-12 show that the implementation of a preferential lane is more
effective in reducing travel times and attracting demand than the implementation of enhanced
boarding and TSP only. The results also show that greater frequency shares (2:1) again lead to
shorter travel times, greater productivity for the limited-stop service, greater increases in
ridership, and higher percentage of limited preferred riders.
129
Table 6-12
7 9 1h Street
Corridor Scenario 2: Moderate BRT Services
iviarel anare
Local Preferred
Limited Preferred
Choice
Demand Split
Local
Limited
Average Passengers per Trip
Local
Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
Local
Change
Limited-Stop
Change
Ridership
After without branding
Change
After including branding
Total Change
15.4%
15.6%
69.1%
8.2%
20.6%
71.3%
13.8%
17.6%
68.5%
7.0%
22.9%
70.1%
13.5%
19.0%
67.5%
6.7%
24.5%
68.8%
100%
-
43.0%
57.0%
32.4%
67.6%
41.5%
58.5%
30.8%
69.2%
40.5%
59.5%
30.1%
69.9%
-
86.1
92.5
81.8
83.6
87.3
89.2
84.0
79.6
89.3
87.1
85.4
77.7
90.2
-
15.6
0.0%
14.7
-5.6%
14.5
-6.9%
15.3
-1.4%
15.0
-3.5%
13.7
-12.0%
13.3
-14.4%
11.5
0.0%
12.7
10.5%
15.2
32.5%
13.0
12.9%
15.0
30.5%
11.4
-0.7%
14.5
26.5%
11.6
1.0%
14.5
25.9%
12.9
12.2%
18.1
57.3%
13.1
13.9%
18.0
56.7%
17.0
48.0%
4203
8.5%
4440
14.7%
4251
9.8%
4532
17.0%
4026
4.0%
4326
11.7%
4108
6.1%
4463
15.3%
4483
15.8%
4990
28.8%
4577
18.2%
5172
33.6%
4743
22.5%
5595
44.5%
-
-
3872
0.0%
-
-
100%
86.1
12.6
-19.2%
-
Note: The high demand increase due to ridership in the limited-stop only configuration is explained by the fact that
the methodology used multiplies the branding factor times the base ridership of the limited-stop service. For the
limited-stop only case this is 100% of the demand prior the implementation of the new service.
Note: The Average Passengers per Trip, the Service Split, and the speeds do not include the change in ridership.
The BRT only scenario shows, as in Route 66, the best performance (travel time reductions, and
newly attracted riders). Figure 6-14 shows in detail the changes in average passenger travel time
for different trip lengths. In contrast with the Scenario 1 only limited-stop service configuration,
the BRT only configuration provides savings for passengers across all trip lengths; however, the
implementation of this strategy will force all passengers living in the vicinity of a local stop to
walk to a combined stop.
130
-5
E
-10
C
a -
Co -15
0
Trip Length (Miles)
Figure 6-14 Change in travel time by trip length for the moderate BRT only service configuration
Large ridership increases are shown from the branding; however, the ridership forecast due to
this effect has a high degree of uncertainty that is the result of assuming the implementation of
all BRT elements that account to the "branding effect" (and following the methodology)
described in the TCRP Report 118 (2007). In addition, these ridership gains are not always
achievable and/or may be constrained by the available capacity.
Branding is a major component of the predicted ridership gains; however, the ridership forecast
due to branding has a high degree of uncertainty that is the result of assuming the
implementation of all BRT elements that account for the "branding effect" (and following the
methodology) described in the TCRP Report 118 (2007). These ridership gains are not always
achievable and/or may be constrained by the available bus capacity.
Extra capacity may be needed with the implementation of BRT services. Table 6-13 shows the
effect of the new ridership on demand split and average passengers per trip assuming no
additional effect on running times. To assign the extra demand to both services, it was assumed
that new riders attracted by increases in level of service would have the same market split as
current riders and that new riders attracted by branding would take the BRT service. The results
show that the local and BRT services can have increased demand (change in passengers per trip)
with respect to the all-local base scenario of up to 17% and 47% respectively.
131
Table 6-13
7 9 th
Corridor Scenario 2 including the effect of new demand: Moderate BRT Services
101
0 12
None
None
None
None
None
Stops
Average distance (mi)
Right of Way
Enhanced Boarding
TSP
Frequjency Share:
-
Headway(min) Limited/Local
Demand
/4
31
0 37
Preferential
Pre payment
2 door buses
Low-floor buses
Yes
None
Pre payment
2 door buses
Low-floor buses
Yes
Preferential
None
None
None
None
3:2
2:1
3:2
2:1
3:2
2:1-
6.67/10
6/12
6.67/10
6/12
6.67/10
6/12
4!-
Local
1807
1380
1672
1272
1815
1379
-
Limited
2633
3153
2654
3191
3174
3793
5595
100%
-
41%
59%
30%
70%
39%
61%
28%
72%
36%
64%
27%
73%
100%
86.1
100
17%
98
13%
92
7%
105
22%
93
8%
98
14%
85
-2%
106
24%
101
17%
118
37%
92
7%
126
47%
-
Service Split
Local
Limited
Average Passengers per Trip
Local
Change (Respect to all local)
Limited-Stop
Change (Respect to all local)
-
-
124
44%
To estimate if extra capacity is need, it is necessary to examine the maximum load point of the
route and observe if the extra demand for each service can be served by the same number of
buses. Figure 6-15 shows that currently Route 79 has a maximum load of 54 passengers per bus.
If demand on the local service is increased by 17%, the maximum load point using the same
number of buses would be 64 people. Therefore, the capacity provided is not enough given a bus
capacity of 55 passengers.
Max Load Point = 54
60
50
40
0 30
20
10
0
20000
40000
60000
Distance (Miles)
Figure 6-15 Route 79 Load Profile
The new demand for the local service (in the case of a 17% increase), would require one extra
local bus per hour. This number is computed as: 17% extra riders per bus trip, under a 10132
minute headway, are equal to 64 passengers (54 x 1.17) per bus, this means that 9 more seats are
needed per bus. Therefore, the extra demand is equal to 54 passengers per hour (9 x 60/10) that
could be served with one extra 55-passenger bus.
The 47% demand increase for BRT buses would mean that the maximum load point would be 80
passengers. However, in this case this demand can be served with the proposed headway if
articulated buses are used (since they have a 90-passenger capacity).
6.2.4
Scenario 3: Full BRT
Different full BRT service configurations are modeled in this sub-section. As in scenario 2, four
different elements are tested: service frequencies, right-of-way segmentation, enhanced boarding
elements, and Transit Signal Priority (TSP). The combined stops selected are the same as in
scenario 2 (see Appendix B). All the scenario 3 combinations have a total (combined) of 15 bus
trips per hour (the same as the current local service).The BRT elements include a completely
segregated lane, buses with three doors with off-vehicle fare payment, and a 35% reduction in
traffic light delays due to TSP (see section 2.4.2). The configurations tested include three
frequency shares (3:2, 2:1, only limited-stop) and three BRT elements (segregated lane only;
enhanced boarding and TSP only; and preferential lane, enhances boarding and TSP).
Table 6-14 shows full BRT effects on all running time components. Decreases in running times
range from 14.0 minutes to 34.0 minutes for the limited-stop service; and the change in running
times for the local service ranges from a 0.2 minute increase to an 18.0 minute decrease. The
effect of changes in total demand was not included in the measures to avoid accounting for
second order effects such increases on running times (given longer dwell times) that would lead
to increases on travel times, and changes in the market share and demand split
133
Table 6-14 Running times components in the
Local
Movement
Traffic
Dwell
Stop Lights
Total
Limited
Movement
Traffic
Dwell
Stop Lights
Total
7 9
th
Street Corridor Scenario 3: Full BRT Services
37.5
8.6
10.4
7.2
63.7
0.0
0.0
0.0
0.0
0.0
28.2
0.0
11.3
7.2
46.7
-9.4
-8.6
0.9
0.0
-17.1
28.2
0.0
10.3
7.2
45.7
-9.4
-8.6
-0.1
0.0
-18.0
37.5
8.6
10.6
7.2
63.9
0.0
0.0
0.2
0.0
0.2
37.5
8.6
9.8
7.2
63.1
0.0
0.0
-0.6
0.0
-0.6
28.2
0.0
10.6
7.2
46.0
-9.4
-8.6
0.2
0.0
-17.7
28.2
0.0
10.3
7.2
45.7
-9.4
-8.6
-0.1
0.0
-18.0
-
-
-
-
21.5
0.0
9.4
7.2
38.1
-16.0
-8.6
-1.0
0.0
-25.6
21.5
0.0
10.0
7.2
38.8
-16.0
-8.6
-0.4
0.0
-24.9
32.8
8.6
3.6
4.7
49.6
-4.7
0.0
-6.9
-2.5
-14.1
32.8
8.6
3.7
4.7
49.8
-4.7
0.0
-6.7
-2.5
-14.0
21.5
0.0
3.6
4.7
29.7
-16.0
-8.6
-6.9
-2.5
-34.0
21.5
0.0
3.6
7.2
32.3
-16.0
-8.6
-6.8
0.0
-31.4
-
-
23.8
0.0
3.8
7.2
34.8
-13.7
-8.6
-6.6
0.0
-28.9
Note*: For the limited-stop only service configuration, it was assumed that the buses serve all the stops. For all the
other configurations it was assumed that the limited-stop service serves an average of 88% of the local stops (see
section 5.4)
Note: The estimated Running Times do not include the effect of new demand
Table 6-15 shows the six indicators for the seven tested configurations. All configurations show
a better performance (higher speeds, shorter travel times, ridership increases, etc.) than the base
scenario and the moderate BRT scenario. The effect of changes in total demand was not included
in the indicators to avoid accounting for second order effects such as increases in running times
(given longer dwell times) that would lead to increases in travel times, and changes in the market
share and demand split.
The results presented in Table 6-15, show that all configurations perform better than the base and
the moderate BRT scenario with higher speeds, shorter travel times, higher percentage of limited
preferred riders, greater ridership increases, etc. The results show that higher frequency shares
lead to greater reductions in travel times, greater productivity for the limited-stop service, and
greater increases in ridership. The implementation of preferential treatment alone in the
7 9 th
corridor is substantially more effective in reducing average travel time and attracting demand
than implementing enhanced boarding and TSP alone.
134
Table 6-15
7 9
th
Street Corridor Scenario 3: Full BRT Services
MarKet bnare
16.3%
15.1%
68.6%
8.6%
20.4%
71.0%
13.7%
17.9%
68.4%
6.9%
23.3%
69.8%
14.1%
19.2%
66.7%
7.3%
23.7%
69.0%
100%
-
43.7%
56.3%
32.8%
67.2%
41.3%
58.7%
30.6%
69.4%
41.1%
58.9%
30.8%
69.2%
-
86.1
94.1
80.7
84.6
86.8
88.9
84.2
79.0
89.6
88.5
84.5
79.5
89.3
-
15.6
0.0%
12.8
-17.5%
12.6
-18.7%
15.2
-2.1%
14.9
-4.3%
11.8
-24.4%
11.8
-24.5%
11.5
0.0%
-
15.7
36.6%
19.2
67.0%
16.0
39.4%
18.9
64.4%
11.5
-0.3%
14.7
28.3%
11.6
1.0%
14.7
28.0%
15.9
38.5%
24.6
114.3%
16.0
39.4%
22.7
97.2%
21.0
82.9%
3872
0.0%
4714
21.7%
4992
28.9%
4761
23.0%
5093
31.5%
4054
4.7%
4355
12.5%
4141
6.9%
4497
16.1%
5018
29.6%
5519
42.5%
5012
29.4%
5601
44.7%
5157
33.2%
6008
55.2%
Local Preferred
Limited Preferred
C~hoice
Demand Split
Limited
Local
Average Passengers per Trip
After: Local
After: Limited-Stop
Average Travel Time (min)
After
Change
Speed (mph)
After: Local
Change
After: Limited-Stop
Change
Ridership
After without branding
Change
After including branding
Total Change
-
-
100%
86.1
11.1
-28.8%
-
Note: The high demand increase due to ridership in the limited-stop only configuration is explained by the fact that
the methodology used multiplies the branding factor times the base ridership of the limited-stop service. For the
limited-stop only case this is 100% of the demand prior the implementation of the new service.
Note: The Average Passengers per Trip, the Service Split, and the speeds do not include the change in ridership
The largest ridership increases and highest reduction in travel times are obtained in a BRT only
scenario (travel time reductions, and new attracted riders). Figure 6-16 shows in detail the
changes in average passenger travel time for different trip lengths. As for the moderate BRT
scenario, this configuration provides travel time savings for passengers across all trip lengths.
However, the implementation of this configuration will substantially affect all passengers living
in the vicinity of a local stop and the passengers with disabilities or other mobility constraints.
135
Figure 6-16 Change in travel time by trip length for the full BRT only service configuration
Again, branding is a major components of ridership gains; however, the ridership forecast due to
this effect has a high degree of uncertainty that is the result of assuming the implementation of
all BRT elements that account for the "branding effect" (and following the methodology)
described in the TCRP Report 118 (2007). These ridership gains are not always achievable
and/or may be constrained by the available bus capacity.
Extra capacity may be needed with the implementation of BRT services. Table 6-16 shows the
effect of the new ridership on demand split and average passengers per trip assuming no effect
on running times. To assign the extra demand to both services, it was assumed that new riders
attracted by increases in level of service would have the same market split as current riders and
that new riders attracted by branding would take the BRT service. The results show that the local
and BRT services can have demand increases (change in passengers per trip) with respect to the
all-local base scenario of up to 33% and 57% respectively.
136
Table 6-16
7 9 th
Corridor Scenario 3 including the effect of new demand: Full BRT Services
Hoadwby
Demand
Local
Limited
Service Split
Local
Limited
1677
2679
1272
3226
2063
3456
1541
4061
6008
100%
-
41%
59%
31%
69%
38%
62%
28%
72%
37%
63%
28%
72%
-
86.1
115
33%
109
26%
104
21%
118
37%
93
8%
99
15%
85
-2%
108
25%
115
33%
128
49%
103
19%
135
57%
-
Average Passengers per Trip
Local
Change (Respect to all local)
Limited-Stop
Change (Respect to all local)
1559
2062
930~ 3534
-
100%
134
55%
To estimate if extra capacity is need, it is necessary to examine the maximum load point of the
route and observe if the extra demand for each service can be served by the same number of
buses. Figure 6-15 shows that currently Route 79 has a maximum load point of 54 passengers. If
demand on the local service is increased by 33%, the maximum load point using the same
number of buses would be 72 people. Therefore, the capacity provided is not enough given a bus
capacity of 55 passengers.
The new demand for the local (in the case of a 33% increase), would require two extra local
buses per hour. This number is computed as: 33% extra riders per bus trip, under a 10-minute
headway, are equal to 72 passengers (54 x 1.33) per bus, this means that 18 more seats are
needed per bus. Therefore, the extra demand is equal to 105 passengers per hour (18 x 60/10)
that can be served with two extra 55-passenger buses.
The 57% extra demand for BRT buses would mean that the maximum load point would be 85
passengers. However, in this case this demand can be served with the proposed headways if
articulated buses are used (since they have a 90-passenger capacity).
6.2.5
Summary of 7 9 th Corridor Findings
The implementation of a conventional limited-stop service overlapped with a local service is not
137
recommended since it will not improve the corridor performance. This poor performance is the
result of the route characteristics, specifically the current high speed for the local service (11.49
mph) and the small number of stops serviced per trip (35 of 101 stops).
On the other hand, a BRT service overlapped with a local service can be effective. If this
configuration is implemented, right-of-way preferential treatment is key to achieve better
performance since it can increase the movement speeds and reduce the traffic time. The
recommended service frequency share is 2:1. A full BRT system could reduce travel times by up
to 24% while a moderate BRT system can reduce that by at most 14%. If service speeds and
travel time want to be improved further, an aggressive TSP scheme could be implemented
(reducing in more than 35% the traffic lights time). If BRT configurations are implemented, it is
strongly recommended to use articulated buses for the BRT services to provide the extra capacity
that might be needed. In addition, one or two additional conventional local buses per hour might
be needed as the result of demand increase.
As for Route 66, the analysis show that BRT only configurations provide the best performance in
terms of travel time reductions and ridership increases; however, implementing such a
configuration can be controversial since it will force people with walking disabilities to increase
their access and egress times.
138
7
CONCLUSIONS AND RECOMENDATIONS
This chapter summarizes the results of this research in five sections. The first section briefly
describes the antecedents, the thesis findings, and the methodology developed for modeling and
evaluating limited-stop or Bus Rapid Transit (BRT) services overlapping with local services. The
second section establish general recommendations for the potential implementation and design of
the previously mentioned services based on the results of the application of the model to the
Chicago Avenue and 79th Street corridors in the city of Chicago. The third section gives specific
recommendations to the Chicago Transit Authority (CTA) regarding their deployment strategy
for BRT and limited-stop services. The fourth section describes the model limitations. The last
section offers suggestions for future work on this subject.
7.1
Summary
Many transit agencies in the US run BRT, limited-stop and local bus services in corridors with
high demand. The combinations of BRT or limited-stop services overlapping with local services
have the potential to benefit transit agencies and riders by reducing passenger travel times and
bus running times. However, in the context of a resource neutral strategy, the implementation of
a strategy with BRT (or limited-stop) services overlapping with local services leads to an
increase in access (and egress) time and waiting time for some passengers. Therefore, agencies
face trade-offs between reducing bus running times, implementation cost (in the case of BRT),
and reducing total passenger travel time when designing a service plan for this strategy. Different
configurations (varying bus stop locations, vehicle types, frequencies, fare technology, levels of
right-of-way segregation and Transit Signal Priority technologies) will result in different
operational and infrastructure costs for the agency, as well as in different levels of service for the
passengers.
Transit agencies are interested in the implementation of these strategies in order to increase the
operational speed (or running time savings), which in turn produces passenger travel time
savings, changes in productivity, and potential changes in ridership. The main objective of this
thesis was to develop a methodology for modeling and evaluating service configurations of
conventional limited-stop or BRT services overlapping with local services. This methodology is
139
an enhancement of the one developed by Schwarcz (2004). It can help transit agencies choosing
more effective service configurations when implementing these services and encourage them to
avoid evaluating the success of a BRT (or limited-stop) service strategy based on only the
increases in operational speeds but instead to consider different effectiveness measures that
account for broader customer and agency impacts. These measures are: market share2 5 , demand
split, average passengers per trip, average travel time (including, access, egress, waiting, and invehicle time), service speeds, and changes in ridership.
Chapter 2 of this thesis summarized the methodology proposed by Schwarcz' thesis and pointed
out its limitations (including its inability to predict ridership changes and its deterministic
approach to assign passengers to markets). It also reviewed the state-of-the-art in estimating
ridership changes when conventional limited-stop and BRT limited-stop services are
implemented, how to model passenger choices when a limited-stop (or BRT) service overlaps
with a local service, and how to model running times for limited-stop and BRT services. Lastly,
the chapter reviews selected cities' experiences with the aforementioned services.
Chapter 3 provided a methodology for predicting changes in ridership when a limited-stop (or
BRT) service overlapping with a local service is introduced. The proposed methodology was
built upon prior research and the experience of the Chicago Transit Authority (CTA) with its
limited-stop X-Routes. It was found that there are two components that account for ridership
increases when the aforementioned services are implemented. The first component is the result
of the reduction of passengers travel time (including access, egress, waiting, and in-vehicle
time); it was found that ridership increases are more accrue for longer trips. The data from the
Ashland and the Cicero corridors showed that travel time elasticities vary by trip length. These
values found could be applied in modeling corridors with similar characteristics in Chicago;
however, for predicting ridership changes in other US cities, it is strongly recommended to
obtain the elasticities following the methodology of Chapter 3 rather than using the values found
25 Waiting
for the local service at the closest local stop, walking to the nearest combined stop and waiting for the
BRT (or limited-stop) service, or walking to the nearest combined stop and take the first bus that comes.
140
in this research. The second component that accounts for ridership increases is the branding
element (only valid in BRT scenarios) that, according to literature, can account for up to 25% in
ridership gains. The methodology proposed for accounting for the BRT branding elements is that
proposed by TCRP Report 118 (2007).
Chapter 4 developed a probabilistic approach to assigning passenger demand. The previous
methodology (developed by Schwarcz) uses a deterministic approach that establishes that all
passengers choose the strategy (wait for the local service, wait for the limited-stop service, or
take the first service that comes) that minimizes their weighted travel time ignoring tastes and
preference variations among passengers. The probabilistic approach uses a path-size logit model
that was built using a set of surveys carried out on the X-routes and local routes serving the
Ashland and the Cicero corridors in Chicago. The final product was a set of utility functions for
each strategy that, as expected, showed that the out-of-vehicle time component is more onerous
that the in-vehicle time. The parameters of the utility functions showed that, for the case of the
surveyed limited-stop services of Chicago, if all the variables are equal (i.e. out-of-vehicle and
in-vehicle times) the preferred strategy is to wait for the local bus, the second favored strategy is
to take the first bus that comes, and the least favored strategy is to wait for the limited-stop
service. The surveys showed that for the studied corridors at least 13% of riders are not familiar
with the limited-stop service; that 22% of the passengers prefer waiting for the local service,
11% of the riders prefer waiting for the limited-stop service, and 67% take the first bus that
come; and that 22% of the passengers that take the first bus that come wait for the limited-stop
service if they see it coming behind the local bus. As with the results of Chapter 3, the estimated
utility function parameters and survey analysis results could be applied to corridors with similar
characteristics in Chicago; however, for modeling the passenger choices in other US cities, it is
strongly recommended that site-specific utility functions be derived following the methodology
of Chapter 4 rather than using the parameters found in Chapter 4.
Chapter 5 developed an improved methodology (putting together the previous methodology with
the findings of Chapters 2, 3 and 4) for modeling and evaluating BRT (or limited-stop) service
configurations overlapping with local services. The model uses inputs such as the frequencies of
the limited-stop (or BRT) and local service, stop locations, the distance between stops, the
141
demand at the stop level, the running times for both services26 , and the travel time components.
The model starts with the estimation of the Origin-Destination (OD) matrix of the current local
service using the boardings and alightings at the stops and an Iteration Proportional Fitting (IPF)
methodology.
After the OD matrix is estimated, the model performs the passenger assignment process in three
steps. In the first step, the model computes the utility -which includes access, egress, waiting,
and in-vehicle components- for every possible OD combination for the three strategies the
passengers may choose: wait for the local service, wait for the limited-stop service, and take the
first bus that comes. If there is no available information about the access and egress times, the
model assigns a zero access/egress time to the ODs served only by the local service and
estimates the extra walking times between the local and combined stops for the passengers
boarding and/or alighting at stops served by the limited-stop service. The waiting times are
estimated based on the proposed service frequencies and on the estimated changes in the
headway coefficient of variation (i.e. service reliability). The in-vehicle times are estimated
based on the trip distances and the projected speeds (running time estimate).
In the second step, the model performs the market classification. In this step the model computes
the probability of the three strategies (local, limited-stop, or first bus that comes) for each OD
pair and then it aggregates the data. With the estimated probabilities, the percentage change in
passenger travel time with respect to the base scenario is computed and then the travel time
elasticities are applied to estimate the changes in ridership. In the third step, the model assigns
the passengers to the bus stops and to the route (local service or limited-stop service).
With the results of the passenger assignment, the model computes the evaluation measures to
evaluate a service configuration, including: market share, demand split, average passengers per
trip, average travel time, service speeds, and changes in ridership.
The model requires 1 or 2 iterations, since the running times are a function of the demand (affecting the dwell
times). Therefore, when the outputs of the model are obtained, they need to be checked against the input demand
split assumption for each of the two services.
26
142
Chapter 6 applied the model to the Chicago and
7 9
th
street corridors in Chicago. Different
scenarios of limited-stop and BRT services overlapped with local services were tested, including
variations in stop spacing, service frequencies, and BRT elements such as: right-of-way,
enhanced boarding, and Transit Signal Priority (TSP). The outputs of the modeling process were
used to evaluate the potential improvements from introducing limited-stop and BRT services in
the selected corridors and understanding how the different BRT elements, the stop spacing, and
the service frequencies, affect the performance of the corridors.
7.2
General Recommendations
The following sub-sections present general recommendations for corridor selection and service
configurations for the studied services based on the modeling results of Chapter 6, and on the
general Chicago experience with limited-stop services.
7.2.1
Corridors/Routes with Potential for introducing BRT and/or Limited-Stop Services
The following conditions are desirable for corridors/routes being considered for implementation
of limited-stop (or BRT) service (overlapping with local service).
High Frequency
The limited-stop service overlapping with a local service strategy is feasible only in corridors
served by an existing high frequency, high ridership route with headways of 8 minutes or less. In
this way, the maximum headway on either service variation would not exceed 15 minutes.
Implementing a limited-stop service in a corridor with low frequency local service (e.g. 15
minute headways) would lead to very long headways for the new local and limited-stop services
(e.g. 30 minutes for each in the case of a 1:1 frequency split). Consequently, implementing a
limited-stop service under this condition would increase the average travel time and result in
most, if not all, passengers walking to a combined stop and boarding the first bus that comes.
Generally, the highest demand corridors which support existing headways of 5 minutes or less
are the best candidates for new limited-stop overlapping service.
143
Long PassengerTrip Lengths
Longer average passenger trip lengths (greater than about 3 miles) are desired since it increases
the number of limited preferred passengers and the likelihood of attracting new riders. The
analysis of Chicago corridors where limited-stop services have been implemented (see Chapter
3) showed that longer trips have larger travel time elasticities than shorter trips. For longer trips,
the reduction in in-vehicle time more than compensates for the increases in access, egress and
waiting times for resource neutral implementation strategies.
Concentrationof Origins and Destinations
High concentration of stop origins and destinations is desired since it allows the limited-stop
buses to serve most of the demand without increasing access and egress times for the limited
preferred and choice passengers. These riders represent more than 60% of the market in the
studied Chicago corridors where limited-stop services have been introduced (see Chapter 4).
Greater Use of Low-Demand Stops
It is important to examine the low-demand stops especially when the only corridor improvement
is the implementation of a limited-stop service (with no other BRT elements). In this context, the
only source of speed increases is the number of actual skip stops (the difference between the
average number of stops served by the local service and the average number of stops served by
the limited-stop service). This means that a limited-stop service would be faster than the local
service only in a corridor where the remaining local stops demonstrate prior demand (boardings
and alightings). In other words, skipping stops that are not used neither increases the speed of the
limited-stop service nor reduces the speed of the current (or future) local service. For the case of
Chicago, each skipped stop saves an average 35 seconds of running time of the limited-stop
service.
Heavy Traffic Congestion, Long Dwell Times, and Many Stop Lights
Corridors with heavy traffic congestion, longer dwell times, and many stop lights are ideal
candidates for BRT enhancements such as an exclusive-segregated lane, enhanced boarding
144
elements, and Transit Signal Priority (TSP). The potential of each BRT element (exclusive rightof-way, enhanced boarding, and TSP) depends on the corridor conditions. An assessment of the
amount of time a bus in a corridor spends moving, interacting with traffic, at bus stops serving
demand, and at stop lights would allow transit agencies to determine the most effective BRT
elements to include.
7.2.2
BRT and Limited-Stop Service Configuration Design
The design elements examined in this thesis are stop spacing, service frequency split, and BRT
elements such as right-of-way segregation, enhanced boarding elements, and TSP. General
recommendations regarding these elements are presented below:
Stop Spacing
A successful BRT (or limited-stop) service needs to serve the higher demand stops whether or
not they are close to each other; therefore, there is not a general "optimal" stop spacing. Instead,
the characteristics of each corridor define the most effective stop spacing strategy. Some
corridors may even require a clustered (local) configuration of stops in a segment with highly
concentrated demand and a wider distribution of stops in other segments.
The methodology to select stops followed in Chapter 6 is similar to that proposed by El-Geneidy
and Thtreault (2008). The methodology consists of ranking stops according to the demand
(boardings and alightings) and converting the most popular stops into combined stops. This
could be complemented by looking at the Origin-Destination (OD) matrix and taking the most
popular OD stop pairs. Finally, if major gaps are noticed between the selected combined stops,
some additional stops could be selected to fulfill a minimum spacing requirement.
The stop selection methodology includes other considerations such as including major transfer
points with other bus and rail services, and choosing a set of combined stops which are the same
in both directions to avoid confusing passengers.
145
Service Frequency Split
The service frequencies contemplated in this research always looked at resource neutral
strategies where the combined frequencies of the local and limited-stop (or BRT) buses serving
the studied corridor are equal to the frequency of the local service prior to the implementation of
the new service. This implies that the waiting time for some passengers (local preferred and
limited preferred) will be increased.
By examining the modeling results of the studied corridors and CTA operating experience, it can
be established that a frequency share (ratio of bus trips that are provided on the limited-stop
service to trips provided on the local service) should be at least 1:1 for limited-stop services
(with no other BRT element) overlapping with local services. However, it is desirable to use
larger ratios (i.e. 3:2 or 2:1) since those configurations result in more even passenger distribution
between the local and the limited-stop services. In other words, 3:2 and 2:1 configurations lead
to scenarios in which the limited-stop service is equally or more crowded than the local service.
When evaluating different frequency shares, it was found that 1:1 ratios provide faster limitedstops service than larger frequency shares at the expense of carrying a lower demand with shorter
overall dwell times.
For BRT services overlapping with local services, a frequency share of 1:1 demonstrates poor
performance since the demand for the local service is usually greater than for the BRT service.
Larger frequency splits (i.e. 3:2 or 2:1) show better performance by evening the demand between
the local and the BRT service.
Right-of-way Segregation
The effectiveness of exclusive rights-of-way depends on the corridor characteristics (especially
the level of traffic congestion) and the segregation level selected (preferential lanes, exclusive
lanes at grade and grade separated exclusive lanes). A corridor with a low movement speed
(which is assessed when the running times are broken into movement, dwell, traffic, and traffic
signal times) would benefit more from the implementation of an exclusive right-of-way than a
corridor with a higher movement speed.
146
Through the modeling of CTA Routes 66 and 79 in Chicago it was found that the enforcement of
the exclusive or preferential lane is a major element in increasing the service speed in the
corridor. Additionally, the implementation of this element not only benefits the BRT service but
also the local service on the same street. However, the right-of-way implementation requires
higher capital cost and may produce a strong opposition from car drivers who would be
negatively affected if, for example, a parking or travel lane is removed.
Enhanced boardingelements
The effectiveness of the enhanced boarding elements depends on the dwell time's share of the
running times. In the studied corridors, the dwell time was a not a large component of the
running time (around 20%); therefore, the reduction of dwell times may not have a large effect
on running times and passenger travel time reduction. The enhanced boarding elements include a
large spectrum, from partially or completely eliminating the use of cash by encouraging the use
of smart cards to a free and level boarding through multiple doors.
It is important to recall that the enhanced boarding elements not only reduce dwell time and
produce running time savings but also attracts new demand due to the branding effect. The
inclusion of low-floor buses with multi-door access and attractive stations can account for up to
60 of the 100 points proposed in the TCRP Report 118 methodology for accounting for the
branding effects.
Transit Signal Priority(TSP)
As with the other BRT elements, the effectiveness of the implementation of TSP depends on the
share of time that the buses spend at traffic signals and on the different technologies and
elements that are used. The literature and the transit agencies provide a wide range of potential
time reductions, from a 5-second saving per intersection along the San Pablo Avenue in Oakland
(TCRP Report 118, 2007) to a 100% reduction in traffic light delays in Zurich. According to the
TCRP Report 118 (2007), "travel time savings associated with TSP in North America and
Europe have a typical reduction of 8% to 12%". For the case of the Ventura and
Wilshire/Whittier Corridors in Los Angeles, where 20% of the base running time was spent at
147
traffic signals, the implementation of TSP reduced signal delay by 35% (Transit Priority
Evaluation. Evaluation Report for the city of Los Angeles, 2003).
Furth 27 acknowledges the difficulty and lack of literature regarding the prediction of travel time
reductions as a result of the implementation of a TSP strategy. For assessing the percentage
reduction in time spent at traffic lights Furth suggests using a range of:
*
10 to 20% traffic delay reduction for the typical timid priority applied in the US
* PLUS 20 to 30% further traffic delay reduction from having a queue jump lane
* PLUS 20% further signal delay reduction from using intelligent and aggressive priority
tactics.
7.3
CTA Recommendations
The implementation or modification of X-Routes (limited-stop services) and BRT services
requires careful analysis since not every route of the system offers the potential for passenger
travel time and running time savings. An evaluation framework, such as the one proposed in this
thesis, should be applied looking at the passenger travel time, running times, productivity, etc. A
configuration of a conventional limited-stop service overlapping a local service may not produce
the desired results in the
7 9
th
Streets and Chicago Avenue corridors where there would not be a
reduction in passenger travel times. However, a configuration including a BRT service
overlapping a local service may be effective in the same corridors since it would reduce
passenger travel times and running times, and attract new riders.
Based on the X-Route experience and the modeling results, CTA should move forward, having
configurations with frequency share (ratio of limited-stop to local buses) greater than 50% for
limited-stop services and greater or equal to 60% for BRT services. The reason to implement
Interview by author with Professor Peter Furth. Department of Civil and Environmental
Engineering,
Northeastern University, 2010
27
148
such configurations is to increase the share of limited preferred riders and to carry more
passengers on the limited-stop (or BRT) service than on the local service.
In a budget constrained scenario, an analysis with the proposed evaluation framework is
recommended in order to decide either to abolish the existing limited-stop services and increase
the local service frequency or abolish the local service and increase the limited-stop service with
perhaps a few more stops.
The surveys carried out during March o2009 showed that at least 13% of the passengers are not
familiar with the X-Routes. CTA should look forward to promoting the use of the limited-stop
service and encouraging riders and potential riders to consider these services. Some signs at local
stops indicating where the closest combined stop is and publishing information about the higher
speed of the limited-stop services should be considered. The promotion of these services should
address the large segment of riders (and potential riders) that are Spanish speakers.
Surveys also showed how real time bus arrival information can affect the split of passengers
between the local and the limited-stop services. About 15% of the riders in the Ashland and
Cicero corridors stated that they take the X-service if they see it coming behind the local service.
During the survey field work it was observed that some limited-stop service buses are reluctant
to overtake local service buses. This operational behavior can be especially annoying for the
limited-stop preferred passengers (the ones who wait for the X-service) that do not see a benefit
from the extra waiting and walking. Explicit instruction should be given to drivers to make sure
the X-service always overtakes the local service at the combined stop or in its vicinity.
The results from the modeling process of the Chicago and the 79th street corridors showed how
the exclusive lane element accounts for a large share of the running time savings obtained with
the implementation of BRT elements. For the studied corridors, it was found that a stop spacing
of close to 0.5 miles was not as effective as shorter spacing (between 0.33 and 0.37 miles).
However, the stop spacing is not as important as which stops are selected. The more effective
configurations are ones where the busiest OD pairs are served by the limited-stop service and
where the corridor has highly concentrated ODs.
149
7.4
Model Limitations
The model developed in this thesis still has some limitations:
" The demand assignment process does not account for different variables such as weather,
or different BRT elements such as the availability of real time information, or shelters at
combined stops. The literature found, as described in Chapter 2, that many of the BRT
elements can change passengers' perception due to branding. Therefore, in a BRT service
overlapped with a local service, the assignment obtained with this model should be taken
as a lower bound for the passengers riding the BRT service.
" The ridership branding predictions for BRT scenarios are based on a methodology
proposed in the TCRP Report 118 (2007). The synergy of the BRT elements that allows
up to an extra 25% ridership gain may not be realized in all contexts. For large-scale
investment projects, it is recommended that forecasters use either a four-step travel
demand model or an incremental logit model rather than the methodology proposed in the
TCRP Report 118.
e
The running time estimation methodology is based on limited sources. For example, there
is little information available on the running time reductions possible from different
Transit Signal Priority (TSP) technologies, or estimation of movement times for different
levels of right of way segregation. The running time estimates could also come from a
micro simulation of the traffic in the corridor.
* The improvements in reliability due to the implementation of limited-stop services are
captured by using different assumed values of the COV of headways for the local
preferred, limited preferred and choice customers. Using updated data from different
sources could improve the estimation of the COV for future services and different COVs
could be used at the segment level rather than using a single COV for the whole corridor.
*
Finally, the model is a descriptive evaluation tool, which can be used to evaluate a
specific user-defined service configuration, rather than an optimization tool that finds the
best possible configuration. An optimization model that, for example, maximizes average
travel time savings or maximizes limited preferred customers might be more effective in
improving the design of service plans for limited-stop and BRT services.
150
7.5
Future Research
The methodology and evaluation framework proposed in this thesis to evaluate BRT (or limitedstop) services overlapping with local services has certain limitations that can be addressed by
future researchers.
" Revising the model to make it easier to use and more automated would be a significant
enhancement. At the moment, the data coming from the APC and the AVL systems are
preprocessed to remove outliers and to create the OD matrix. The probabilities of
choosing each strategy, the demand assignment to the local and limited-stop (or BRT)
services, the changes in ridership, etc. are manual, separate step spreadsheet operations. If
these processes were integrated into a single software application, where the inputs are
entered and the outputs are obtained more easily, it would make it more feasible to be
used regularly by transit agencies.
" The model evaluates single configurations; however, the model could be set up to iterate
to find configurations that seek the largest passenger travel time savings under certain
constraints (i.e. same stops in both directions, maximum and minimum distances between
stops, maximum frequencies, etc.)
e
The model currently does not consider the cost of including different elements such as
segregated right-of-way, transit signal priority, and change in vehicle-hours. A
component that includes these costs could help agencies to pursue their goals more
directly and would give an extra dimension to the evaluation framework.
* The model has a basic approach for estimating the running times. The proposed approach
is iterative since it is required as an input to estimate the demand split into the services
(local and limited-stop) and to estimate the dwell times; subsequently, when the demand
split is obtained from the outputs of the model, new estimated of the running times are
performed and the model needs to be run a second time to converge. A new approach that
uses a linear regression of the running times in order to assess the impact of the number
of stops, the average actual served stops, the demands, etc. such as that proposed by ElGeneidy and Thtreault (2008) could be more efficient in assessing the limited-stop and
local service running times. If the effects of BRT components (e.g., a dedicated lane,
151
signal priority, and enhanced boarding elements) need to be included, a micro simulation
approach could be better. Routes/corridors can be evaluated in different segments since
the running time splits between movement, dwell, traffic, and signal times changes along
the corridor. The model could also account for the split of passengers using different fare
media at stations (smart card, cash, magnetic cards, etc.)
* The travel time elasticities found in Chapter 3 cannot be generalized to the US context. A
more comprehensive examination of the relation between the change in the ridership and
the change in passenger travel time due to the implementation of limited-stop services,
including case studies of changes from all-local to all-limited-stop (or all-BRT)
configurations, should be performed in order to obtain a better understanding of the
phenomenon.
* The utility functions estimated in Chapter 4 cannot be generalized to the US context.
Passenger preferences for limited-stop and local services can vary. Additionally, the
functions obtained should be considered a lower bound for a case where a BRT service
overlaps with local service. Theoretically, the BRT service should be more appealing to
the customers than the local services. A stated preference survey with choices between
BRT and local services with different configurations could be an approach to understand
how passengers would choose these services, and how passengers from other
transportation modes could switch to use a BRT service.
The methodology proposed in this thesis is a first step towards the design and evaluation of
limited-stop and express services in a context of high-capacity BRT systems such as
Transmilenio in Bogota where many origin and destinations are served by different local,
limited-stop or express services. Passenger behavior and the design of these services need to
be studied in a similar way to this thesis. However, other variables that will increase the level
of complexity should be accounted for including walking times between platforms in a
station and walking times from origin and destinations to stations, real time information, the
number of available routes to serve an OD and how this level of complexity is understood (or
not) by the customers.
152
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154
APPENDIX A
TYPE I. IN AN X BUS
Route
Stop Location
Direction NB SB Day and Time
1. How many days a week do you ride this bus route?
o 7 days
0 5 to 6 days
0 3 to 4 days
0 1to 2 days
o Less than once per week
2. What is the purpose of the trip you are makingnow?
O Home
O School
O Work/Work related-trip
0 Shopping
0 Social/Recreational trip
0 Hospital/Medical Trip
0 Other
3a. Was the bus stop where you boarded this bus the closestto where you began your trip?
O Yes
0 No
If 3a was NO
3b. H ow many blocks away is the closest bus stop to where youbeganyour trip?
4. H ow many blocks did you walk from where you be gan your trip to the stop where you board the bus?
5. At which stop street or major street intersection will you get off the bud?
Address/Mayor intersection
and
6. H ow many blocks will you walk after you get OFF the bus until you reach your destination?
7. Which bus route do youusually take?
O I always take the first bus that comes (X or local)
0 I usually take the first bus that comes but if I se e the X bus coming behind thelocal I will take the X one
0 I always wait for the X bus
8. If youhad taken the Loc al bus, at which street or major street intersection would you get off the bud?
Address/Mayor intersection
and
9. If youhad taken the Localbus,how many blocks would you walk after you get off the bud?
10. Which of the following statemeds do you most identify with whentaking this route or any other CTA bus routes
withXservice?
O I go directly to the combined (local and X) stop
0 I go to the local stop and if I don't see the loc al bus coming I will start walking to the combined (loc al and X) stop
11. Are you: 0 Male 0 Female
12. Do youhave any difficulties that make it difficult to walk longer distanced?
0 Yes 0 No
155
TYPE II. IN A LOCAL BUS. PASSENGER BOARD AT LOCAL STOP
Route
Stop Location
,y,$pjj
SB Day and Time
1. How many days a week do you ride this bus route?
[ 7 days
[ 5 to 6 days
U 3 to 4 days
0 1 to 2 days
O Less than once per week
2. What is the purpose of the trip you are making now?
O Home
U School
U WorkWork related-trip
o
Shopping
o
Social/Re creational trip
U H ospita/Me dical Trip
o Other
3. H ow many blo cks did you walk from where you be gan your trip to the stop where you board the bus?
4. At which stop stre et or major stre et intersection will you get off the bus?
Address/Mayor intersection
_
and
5. How manyblo cks will you walk after you get OFF the bus util you reach your destination?
6 a. Are you familiar with the X bus and where it goes?
o Yes
U No
If 6a was YES
6b 544of the following statements do you most identify with when taking this route or any other CTA bus mutes
with X s ervic e?
o I usually stay at the lo cal stop and wait for the loc al bus
o If I don't see the loc al bus coming I will start walking to the combined (lo cal and X) stop
7. Are you: 0 Male O F emale
8. Do you have any difficulties that make it difficult to walk longer distanced?
U Yes U No
156
TYPE III. IN A LOCAL BUS. PASSENGER BOARD AT COMBINED STOP
Route
Stop Location
Direction NB SB Day and Time
1. How many days a week do you ride this bus route?
0 5 to 6 days
0 3 to 4 days
0 1 to 2 days
o 7 days
2. What is the purpose of the trip you are makingnow?
O Home
0 School
0 Work/Work related-trip
0 Social/Recreational trip
0 Hospital/Medical Trip
0 Less thanonce per week
0 Shopping
0 Other
3a. Was the bus stop where you boarded this bus the closest to where you be gan your trip?
O Yes
0 No
If3a was NO
3b. How manyblocks away is the closest bus stop to where youbeganyourtrip?
4. H ow many blo cks did you walk from where you be gan your trip to the stop where you board the bus?
5. At which stop stre et or major stre et intersection will you get off the bus?
Address/Mayor intersection
and
6. H ow many blocks will you walk after you get OFF the bus until you re ach your destination?
7a. Are you familiar with the X bus and where it goes?
o Yes
0 No
If 7a was a YES
7b. Which bus route do you usually take?
0 I always take the first bus that comes (X or local)
0 I usually take the first bus that comes but if I see the X bus coming behird the local I will take the Xone
O I always wait for the local
If7a was a YES
7c. you had taken the X bus, at which stre et or major stre et intersection would you get off the bus?
Address/Mayor intersection
and
If 7a was a YES
7d. Wyouhad taken the X bus, how many blocks would you walk after you get off the bus?
8. Which of the following statements do you most identify with whentaking this route or any other CTA bus routes
with X service?
O I go directly to the combined (loc al and X) stop
0 I go to the loc al stop and if I don't see the loc al bus coming I will start walking to the combined (lo c al and X) stop
9. Are you: 0 Male 0 Female
10. Do youhave any difficulties that make it difficult to walk longer distances?
O Yes 0 No
157
APPENDIX B
Route 66 Proposed Combined Stops
1 CHICAGO
2 CHICAGO
3 CHICAGO
4 CHICAGO
5 CHICAGO
6 CHICAGO
7 CHICAGO
8 CHICAGO
9 CHICAGO
10 CHICAGO
11 CHICAGO
12 CHICAGO
13 CHICAGO
14 CHICAGO
15 CHICAGO
16 CHICAGO
17 CHICAGO
18 CHICAGO
19 CHICAGO
20 CHICAGO
21 CHICAGO
22 CHICAGO
23 CHICAGO
24 CHICAGO
25 CHICAGO
26 CHICAGO
27 CHICAGO
28 CHICAGO
29 CHICAGO
30 CHICAGO
31 CHICAGO
32 CHICAGO
33 CHICAGO
34 CHICAGO
35 CHICAGO
36 CHICAGO
37 CHICAGO
38 CHICAGO
39 CHICAGO
40 CHICAGO
41 CHICAGO
42 CHICAGO
43 CHICAGO
44 CHICAGO
45 CHICAGO
46 CHICAGO
47 CHICAGO
48 CHICAGO
49 CHICAGO
Name
+ AUSTIN
+ MAYFIELD
+ MENARD (west leg)
+ WALLER (west leg)
+ CENTRAL
+ PINE
+ LONG
+ LOCKWOOD
+ LARAMIE
+ LECLAIRE
+ LAVERGNE
+ LAMON
+ CICERO
+ KILPATRICK
+ KILBOURN (east leg)
+ KOSTNER
+ KILDARE
+ KEELER
+ KARLOV
+ PULASKI
+ SPRINGFIELD
+ HAMLIN
+ LAWNDALE
+ CENTRAL PARK
+ ST. LOUIS
+ HOMAN
+ SPAULDING
+ KEDZIE
+ SACRAMENTO(west serv.dr.)
+ GRAND
+ CALIFORNIA
+ WASHTENAW
+ ROCKWELL
+ CAMPBELL
+ WESTERN
+ OAKLEY
+ LEAVITT
+ HOYNE
+ DAMEN
+ WOLCOTT
+ PAULINA
+ WOOD
+ ASHLAND
+ BISHOP
+ NOBLE
+ ELIZABETH (east leg)
+ MILWAUKEE
+ OGDEN
+ SANGAMON (west leg)
Scenario
23 Stops 28 Stops
1
1
0
0
0
0
0
0
1
1
0
0
0
1
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
1
1
0
0
0
0
0
1
1
0
0
1
0
1
0
0
0
0
0
1
1
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
1
0
0
0
1
1
0
0
0
0
0
1
1
0
0
1
0
0
0
0
1
1
0
0
0
0
158
Name
No
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
CHICAGO + HALSTED
CHICAGO + 700 WEST
CHICAGO + LARRABEE
CHICAGO + HUDSON
CHICAGO + ORLEANS
CHICAGO + WELLS
CHICAGO + LASALLE
CHICAGO + CLARK
CHICAGO + DEARBORN
CHICAGO + STATE
CHICAGO + WABASH
CHICAGO + RUSH
CHICAGO + MICHIGAN
CHICAGO + MIES VAN DER ROHE
CHICAGO + FAIRBANKS
FAIRBANKS + SUPERIOR
FAIRBANKS + HURON
FAIRBANKS + ERIE
FAIRBANKS + ONTARIO
ONTARIO + FAIRBANKS
FAIRBANKS + OHIO
ILLINOIS + McCLURG
ILLINOIS + PESHTIGO
ILLINOIS + LAKE SHORE
NAVY PIER + TERMINAL
23 Stops 28 Stops
1
0
1
0
1
0
0
1
0
1
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
1
0
1
0
1
0
0
1
0
1
0
0
1
0
1
0
1
0
0
1
0
0
1
0
1
159
Route 79 Proposed Combined Stops
Name
1 79TH STREET + BRANDON
2 79TH STREET + SOUTH SHORE
3 79TH STREET + COLES (west leg)
4 79TH STREET + EXCHANGE AND METRA
5 79TH STREET + MUSKEGON (west leg)
6 79TH STREET + BURNHAM (east leg)
779THSTREET+ MARQUETTEAVENUE
8 79TH STREET + COLFAX
9 79TH STREET + ESSEX
10 79TH STREET + YATES
11 79TH STREET+ OGLESBY
12 79TH STREET + CRANDON
13 79TH STREET + LUELLA
14 79TH STREET + PAXTON
15 79TH STREET + CLYDE
16 79TH STREET+ JEFFERY
17 79TH STREET + BENNETT
18 79TH STREET + CREGIER
19 79TH STREET + EAST END (west leg)
20 79TH STREET + STONY ISLAND
21 79TH STREET + ANTHONY
22 79TH STREET + DORCHESTER
23 79TH STREET + KENWOOD
24 79TH STREET + KIMBARK
25 79TH STREET + WOODLAWN
26 79TH STREET + GREENWOOD
27 79TH STREET + ELLIS
28 79TH STREET + DREXEL
29 79TH STREET + COTTAGE GROVE
30 79TH STREET + LANGLEY
31 79TH STREET + ST. LAWRENCE
32 79TH STREET + EBERHART
33 79TH STREET + KING DRIVE
34 79TH STREET + CALUMET
35 79TH STREET + PRAIRIE
36 79TH STREET + INDIANA
37 79TH STREET + MICHIGAN
38 79TH STREET + WABASH
39 79TH STREET + STATE
40 79TH STREET + RED LINE STATION
41 79TH STREET + LAFAYETTE
42 79TH STREET + PERRY
43 79TH STREET + WENTWORTH
44 79TH STREET + PRINCETON
45 79TH STREET + VINCENNES
46 79TH STREET + EGGLESTON
47 79TH STREET + NORMAL
48 79TH STREET + FIELDING
49 79TH STREET + LOWE
Scenario
27 Stops 31 Stops
1
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
1
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
1
0
1
0
0
0
1
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
1
0
1
0
1
0
0
1
0
0
1
1
0
1
0
0
1
0
0
0
1
160
Name
50 79TH STREET + UNION
51 79TH STREET+ EMERALD
52 79TH STREET + HALSTED
53 79TH STREET + PEORIA
54 79TH STREET + MORGAN
55 79TH STREET + ABERDEEN
56 79TH STREET + RACINE
57 79TH STREET + THROOP (west leg)
58 79TH STREET + LOOMIS
59 79TH STREET + LAFLIN (east leg)
60 79TH STREET + ASHLAND
61 79TH STREET + PAULINA
62 79TH STREET + WOOD
63 79TH STREET + WOLCOTT
64 79TH STREET + DAMEN
65 79TH STREET + HOYNE
66 79TH STREET + HAMILTON
67 79TH STREET + OAKLEY
68 79TH & WESTERN + TERMINAL (South bay)
69 79TH STREET + WESTERN
70 79TH STREET + CAMPBELL
71 79TH STREET + TALMAN
72 79TH STREET + WASHTENAW
73 79TH STREET + CALIFORNIA
74 79TH STREET + FRANCISCO
75 79TH STREET + SACRAMENTO
76 79TH STREET + ALBANY
77 79TH STREET + KEDZIE
78 79TH STREET + COLUMBUS (SOUTHWEST HWY.)
79 79TH STREET + SPAULDING
80 79TH STREET + HOMAN
81 79TH STREET + ST. LOUIS
82 79TH STREET + CENTRAL PARK
83 79TH STREET + LAWNDALE (east leg)
84 79TH STREET + HAMLIN (east leg)
85 79TH STREET + SPRINGFIELD (west leg)
86 79TH STREET + PULASKI
87 79TH STREET + KARLOV (east leg)
88 79TH STREET + TRIPP
89 79TH STREET + KOSTNER
90 79TH STREET + KILBOURN
91 79TH STREET + KENTON
92 79TH STREET + KILPATRICK (west leg)
93 79TH STREET + CICERO
94 79TH STREET + LAMON
95 79TH STREET + LAVERGNE (west leg)
96 79TH STREET + LECLAIRE
97 79TH STREET + LARAMIE
98 79TH STREET + LOCKWOOD
99 79TH STREET + STATE ROAD
100 79TH STREET + CENTRAL
101 CENTRAL+78TH STREET
27 Stops 31 Stops
1
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
161