MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT
MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR
BAY AREA RAPID TRANSIT (BART) STATIONS
A Project
Presented to the faculty of the Department of Civil Engineering
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Civil Engineering
by
Gurpreet Singh Dhaliwal
FALL
2012
©2012
Gurpreet Singh Dhaliwal
ALL RIGHTS RESERVED
ii
MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT
MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR
BAY AREA RAPID TRANSIT (BART) STATIONS
A Project
by
Gurpreet Singh Dhaliwal
Approved by:
____________________________________, Committee Chair
Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE
____________________________
Date
iii
Student: Gurpreet Singh Dhaliwal
I certify that this student has met the requirements for format contained in the University
format manual, and that this project is suitable for shelving in the Library and credit is to
be awarded for the project.
___________________________, Dean
Emir José Macari, Ph.D.
_____________________
Date
Department of Civil Engineering
iv
Abstract
of
MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT
MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR
BAY AREA RAPID TRANSIT (BART) STATIONS
by
Gurpreet Singh Dhaliwal
The impact of Bay Area Rapid Transit (BART) proximity on morning and
afternoon peak-hour vehicle trips generated by Transit-Oriented Apartments (TOAs) was
observed. BART is one of the busiest rail transit system in the U.S. located in the. It
connects San Francisco and the Peninsula region to the East Bay of the San Francisco
Bay Area. Ten TOAs, both in the East Bay and Peninsula region, were selected near ten
BART stations. The morning and afternoon peak-hour volumes were observed from 6:00
a.m. to 9:30 a.m. and 4:00 p.m. to 7:30 p.m., and then compared with the peak-hour trips
estimated by the Trip Generation Manual (8th Edition) published by the Institute of
Transportation Engineers (ITE).
The analysis and comparison of observed trip generation data with ITE estimates
suggests that fewer peak-hour vehicle trips were generated both in the morning and
afternoon, however the impact varied from site to site. Most TOAs showed a reduction in
v
the morning and afternoon peak-hour volumes. In the morning, about 19% fewer vehicle
trips were produced; whereas in the afternoon, about 50% fewer vehicle trips were
produced. It is hypothesized that this reduction in peak-hour trips can be attributed, in
part, to the TOA’s proximity to BART.
______________________________________, Committee Chair
Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE
__________________________
Date
vi
DEDICATION
I dedicate this project to my parents who inspired me and helped me as well to reach this
point.
vii
ACKNOWLEDGEMENTS
I would like to thank my father, mother, and brother for continuously encouraging me to
finish this project. I also cannot overlook the patience and support of my wife and
daughter. Finally, I would like to thank Dr. Kevan Shafizadeh for his invaluable advice
and direction in completing this project.
viii
TABLE OF CONTENTS
Page
Dedication ......................................................................................................................... vii
Acknowledgements .......................................................................................................... viii
List of Tables ..................................................................................................................... xi
List of Figures ................................................................................................................... xii
Chapter
1.
INTRODUCTION ..................................................................................................... 1
2.
BACKGROUND ....................................................................................................... 3
Bay Area Rapid Transit (BART) ..................................................................... 3
Transit-Oriented Apartments (TOAs).............................................................. 4
3.
METHODOLOGY .................................................................................................... 9
Trip Data Collection ........................................................................................ 9
Data Analysis ................................................................................................. 11
Estimation of ITE Trips ................................................................................. 11
Data Comparison ........................................................................................... 12
4.
INFORMATION ABOUT SELECTED TOAs ...................................................... 13
Union City ..................................................................................................... 14
Hayward ......................................................................................................... 16
ix
Fremont .......................................................................................................... 19
Concord.......................................................................................................... 21
Colma ............................................................................................................. 23
South San Francisco ...................................................................................... 25
Walnut Creek ................................................................................................. 27
Berkeley ......................................................................................................... 29
5.
RESULTS AND DISCUSSION ............................................................................. 40
6.
CONCLUSION ....................................................................................................... 53
Appendix A
Observed Data .......................................................................................... 55
Appendix B
Trip Rate For Mid-Rise Apartments......................................................... 65
Appendix C
BART Ridership ....................................................................................... 67
References ......................................................................................................................... 70
x
LIST OF TABLES
Table
Page
1.
Distance Between Selected TOAs and the Nearest BART Stations ....................... 34
2.
Walk Score Scale .................................................................................................... 38
3.
Selected TOAs and Their City’s Walk Scores ........................................................ 39
4.
A.M. and P.M. Peak-Hour Periods for Selected TOAs........................................... 40
5.
Number of Observed A.M. and P.M. Peak-Hour Trips vs. ITE-Estimated PeakHour Trips ............................................................................................................... 42
6.
Observed and ITE-Estimated Peak-Hour A.M. and P.M. Trip Rate ....................... 49
xi
LIST OF FIGURES
Figure
Page
1
BART System Map ................................................................................................... 4
2.
Projected Demand for Housing Near Transit in 10 U.S. Transit Regions ................ 7
3.
Union City – Avalon Apartments............................................................................ 15
4.
Hayward – Montelena Apartment Homes ............................................................... 17
5.
Hayward – City Centre Apartment Homes ............................................................. 18
6.
Fremont – Archstone Apartment Communities ...................................................... 20
7.
Concord – Park Central Apartments ....................................................................... 22
8.
Colma – La Terrazza Apartments ........................................................................... 24
9.
South San Francisco – Archstone Apartment Communities ................................... 26
10.
Walnut Creek – Trinity House Apartment Homes .................................................. 28
11.
North Berkeley – Acton Apartments ....................................................................... 30
12.
Downtown Berkeley – Berkeleyan Apartments ...................................................... 31
13.
Size and Occupancy of Selected TOAs ................................................................... 32
14.
Selected TOA Locations ......................................................................................... 35
15.
BART’s Average Daily Ridership in September & October 2012 ......................... 36
16.
Percent Reduction in A.M. Peak-Hour Trips Compared with ITE ......................... 44
17.
Percent Reduction in P.M. Peak-Hour Trips Compared with ITE .......................... 45
18.
Comparison of Observed A.M. Peak-Hour Trips with ITE .................................... 47
xii
19.
Comparison of Observed P.M. Peak-Hour Trips with ITE ..................................... 48
20.
Comparison of Observed A.M. Peak-Hour Trip Rate with ITE ............................. 50
21.
Comparison of Observed P.M. Peak-Hour Trip Rate with ITE .............................. 51
22.
A.M. & P.M. Peak-Hour Trip Rate for All TOAs Combined ................................. 52
xiii
1
Chapter 1
INTRODUCTION
The purpose of this project was to analyze the vehicle trips generated by Transit Oriented Apartments (TOAs) located in close proximity of Bay Area Rapid Transit
(BART) stations. The focus was on morning and afternoon peak-hour trips generated by
the TOAs.
BART is one of the most prominent and popular public transportation modes
located in the San Francisco Bay Area. It connects East Bay cities (e.g., Fremont,
Oakland, Concord, Pittsburg, Richmond, Dublin) to San Francisco and five Peninsula
cities (Daly City, Colma, South San Francisco, San Bruno, and Millbrae). Possible
reasons behind BART’s popularity include connectivity to major employment hubs
(mainly, San Francisco and Oakland), quite frequent service, severe traffic congestion on
San Francisco Bay Area freeways, and scarce or expensive parking. The congestion on
the freeways, the environmental concerns, and the suburban sprawl have made local,
state, and federal governments recognize Transit-Oriented Developments (TODs) as a
possible sustainable solution to address the above mentioned challenges.
For this project, ten multifamily housing complexes were selected near ten BART
stations (out of 44 stations total). All of the selected Transit-Oriented Apartments (TOAs)
were situated within short walking distance from the nearest BART station. After
meeting property managers and conducting thorough online research of each of all
selected TOAs, it was found that almost all of these complexes were well furnished and
had very low vacancy rates (less than 5%). First, the morning (6:00 a.m. to 9:30 a.m.) and
2
afternoon (4:00 p.m. to 7:30 p.m.) trip data – number of vehicles entering and exiting the
chosen sites – were documented. Then the peak-hour time and volume were computed,
and finally the results were compared with the trip generation data estimated using the
Trip Generation Manual (8th Edition) published by the Institute of Transportation
Engineers (ITE). The percentage difference represented the impact of BART proximity
on the peak-hour trip generation.
This project is significant because most transportation improvements are capacity
based, generally depending upon the peak-hour traffic. So, a minor change in the A.M.
and P.M. peak-hour trip volumes can possibly lead to significant change in the cost and
extent of the improvement projects. Also, TOAs are promoted by all levels of
governments (U.S. GAO, 2009), but it remains uncertain to what extent TOAs really
satisfy the planned objectives.
3
Chapter 2
BACKGROUND
Bay Area Rapid Transit (BART)
BART is the busiest heavy rail transit system in the western U.S. region and the
fifth-busiest in the U.S. (American Public Transportation Association, 2012). It is located
in the heart of the San Francisco Bay Area, connecting East Bay cities such as Fremont,
Oakland, Berkeley, Concord and Pittsburg to San Francisco and Peninsula cities such as
South San Francisco, Millbrae, Colma, and Daly City. It has five routes: Fremont to
Richmond, Fremont to Daly City, Richmond to Daly City, Dublin/Pleasanton to Daly
City, and Pittsburg/Bay Point to Millbrae, as shown in Figure 1.
On a typical weekday, BART carries about 400,000 riders along all 44 stations,
including San Francisco International Airport station (BART, 2012a). Most of the BART
stations have parking facilities available for riders to park their personal vehicles and
commute on BART, but the parking prices depend on the geographical location. For
example, the daily parking fee at the Union City station is $1 and the South San
Francisco station is $2, however it is free at the Richmond station. BART also has
monthly reserved parking and single day reserved parking, varying from station to
station.
To meet the transfer needs or to serve destinations not near the BART stations,
transit connections are available at all stations; for instance, at the Richmond station,
passengers can transfer between BART, Capitol Corridor (Amtrak train that operates
between San Jose and Sacramento), Amtrak San Joaquin (train service that operates
4
between Sacramento and Oakland to Bakersfield), Alameda-Contra Costa (A.C.) Transit
bus service, and the Golden Gate Transit bus service that operates between Santa Rosa,
San Rafael, and San Francisco. All BART stations have at least one transit connection.
Figure 1. BART System Map (BART, 2012b)
Transit-Oriented Apartments (TOAs)
Freeway congestion, air pollution, and suburban sprawl have forced planners and
responsible authorities to discover the viable solutions. One of the main reasons behind
5
these three challenging issues is the land use policy implemented in U.S. for decades. The
relatively lower cost of land near outskirts may encourage suburban sprawl, especially
the housing, although employment generation is mostly near the central business districts,
forcing suburbanites to drive longer distances to work, which contributes to congestion
and air pollution. The longer commute distances on personal vehicles contribute to air
pollution and freeway congestion, which results in lost time and fuel wastage. According
to a U.S. Department of Treasury report, “Traffic congestion on our roads results in 1.9
billion gallons of gas wasted per year, and costs drivers over $100 billion in wasted fuel and
lost time” (U.S. Department of Treasury, 2012). To address these challenges, agencies are
considering the intertwined land use and transportation planning as a potential solution.
The key focus is probably on the land available for development or development in the
vicinity of existing public transit (e.g., rail stations, bus stops).
According to the Center For Transit-Oriented Development (CTOD), a “Transitoriented development, or TOD, is a type of community development that includes a
mixture of housing, office, retail and/or other commercial development and amenities
integrated into a walkable neighborhood and located within a half-mile of quality public
transportation” (CTOD, 2012a). TODs are also referred as transit villages, transitproximate development, or walkable neighborhoods. Center For Transit-Oriented
Development (CTOD) promotes sustainability in transportation and community
development. Since significant numbers of automobile trips are employment related,
mixed-use TODs can possibly reduce the number of trips substantially. According to the
CTOD, almost any location, even suburban, with appropriate transit service can perform
6
well if the employment generation areas are clustered and located in relatively dense
concentrations; the parking control is required, too (Belzer et al., 2011). It also suggests
viewing concentrated versus dispersed employment nodes, not urban versus suburban,
when employment activity is addressed.
As the name suggests, the Transit-Oriented Apartments (TOAs) are the apartment
complexes located in walking distance within a half-mile radius of a public transit
facility. From compactness perspective, TOAs are generally medium- to high-density
development projects. Not only do they help in reducing freeway congestion and air
pollution (especially greenhouse gases which are primarily held responsible for global
warming), they also lead to active and healthy lifestyle, increased public transit ridership,
and improved economic opportunities for low- to mid-income families by freeing up
automobile related expenses and easier access to jobs (Reconnecting America, 2004a). In
comparison to the traditional suburban single family houses, TOAs can accommodate
people of all age groups and incomes since they provide choices in the type of housing
(e.g., lofts, studios, apartments) (Reconnecting America, 2004a).
The demographic changes are also forcing the modification of the land use policy
and planning. By 2050, singles will outnumber typical families, and older Americans will
be the new majority (Reconnecting America, 2004a). So the traditional suburban single
family houses may not meet the future housing needs of single households. CTOD
estimates that about 25% of house seekers, or about 15 million households, in 2030 will
prefer housing near transit, requiring appropriate planning and implementation now to
meet the growing demand (Reconnecting America, 2008).
7
Before altering the land use policy and planning practices in favor of TOAs, it is
necessary to examine the future demand for them. In the ten major metropolitan areas,
where roughly one-fourth of the U.S. population resides, the demand for TOAs will grow
strongly by the middle of next decade, as shown in Figure 2, which depicts the
percentage of TOAs in year 2000 and estimated percentage of TOAs in year 2025
(Reconnecting America, 2004b).
60%
% Regional housholds near transit, 2000
50%
Projected % regional households near transit, 2025
40%
30%
20%
10%
0%
Figure 2. Projected Demand for Housing Near Transit in 10 U.S. Transit Regions
(Reconnecting America, 2004b)
8
All ten metropolitan areas, shown in Figure 2, show a strong demand for TOAs by
2025. The Los Angeles metropolitan area shows the strongest increase in demand (four
times) for TOAs, followed by Dallas, Miami, Portland, and Washington, D.C. The
demand in New York, Chicago, Boston, San Francisco Bay Area, and Philadelphia
metropolitan areas will also grow approximately by one-and-a-half times by year 2025.
9
Chapter 3
METHODOLOGY
The goal of this project was to compare the observed trips generated with the ITEestimated trips for the selected TOAs to evaluate the impact of BART proximity on trip
generation. After selecting the qualifying TOAs, this project was divided into four parts:
1. Collect vehicle trip data collection for the selected TOAs;
2. Analyze of the collected trip data to find the peak-hour periods and volumes;
3. Compute vehicle trip estimates using the ITE Trip Generation Manual; and
4. Compare the observed trip data with the ITE-estimated trip data.
The data collection was the most critical task of this project because the results relied on
its accuracy. The four tasks are explained in detail below.
Trip Data Collection
This critical and laborious task of the project was done in the morning and
afternoon for all selected TOAs. The morning and afternoon data collection times were
from 6:00 a.m. to 9:30 p.m. and 4:00 p.m. to 7:30 p.m., respectively, to find and capture
the peak-hour volumes. Generally, peak hours fall between 7:00 a.m. and 9:00 am and
between 4:00 p.m. and 6:00 p.m., but they can vary. The number of vehicles entering and
exiting the TOAs, and the number of TOA residents utilizing the on-street parking, was
documented in 15-minute increments. The data were collected only on Tuesday,
Wednesday, or Thursday to prevent possible introduction of unusual travel behavior.
Appendix A contains the field observed data (number of vehicle trips generated or
10
attracted by the selected TOAs) in quarter-hour increments. The peak-hour computations
for both morning and afternoon are also shown in Appendix A tables for each selected
TOA.
The number of automobile access points for all TOAs played an important role in
the data collection process. The number of access points and their locations dictated the
number of data collectors needed, not the size of the TOA complexes in this project. A
few TOAs required only one data collector, whereas others required two or more. For
instance, the Union City TOA was the largest among selected sites, but it required only
one data collector since both vehicle entrances/exits were located on the same side of the
building; the on-street parking was located in between the two entrances, so one data
collector could easily track and document the vehicle movements. The Downtown
Berkeley, North Berkeley, and Colma TOAs had only one vehicle entrance and limited
on-street metered parking, so they only required one data collector. Walnut Creek and
Concord TOA were almost like the Union City TOA, so they only required one person in
the field. The South Hayward TOA had three vehicle access points – one emergency
access gate and one exit only gate on the south, and one entrance/exit on the west, but it
required only one data collector because one person at the southwest corner of the
building could easily capture the vehicle movements. The street parking was only
available on one side, so one person could simply track and document the number of
observed trips. Fremont, South San Francisco, and Hayward required two data collectors
because of the vehicle entrances/exits number and location.
11
The availability of limited resources constrained the data collection to one day per
TOA. This study utilized data collectors who were physically present at the TOA, instead
of using automatic vehicle counters (pneumatic tubes) on the automobile access points.
Data Analysis
After the data collection task was finished, the data were analyzed to find the
peak-hour period and volume for the morning and afternoon. The peak-hour and
associated trips were found by adding consecutive 15-minute trip volumes, as shown
highlighted in red in Appendix A. After arriving at the peak hour, the trip counts were
compared with the ITE-estimated vehicle trips.
Estimation of ITE Trips
The peak-hour vehicle trips estimated by the Trip Generation Manual served as
the benchmark for comparing the observed trips. The equations provided in the Land Use
Code 223 of the Trip Generation Manual were used to estimate the A.M. and P.M. peakhour trips for the selected TOAs. To compute the ITE suggested trips for the selected
TOAs, the following peak-hour equations (shown in Appendix B) were used:
T = 0.46 (X) – 14.01
(Weekday, A.M. Peak Hour of Generator)
T = 0.53(X) – 11.27
(Weekday, P.M. Peak Hour of Generator)
where, X is the number of dwelling units, and
T is the average number vehicle trip ends (i.e., trips “generated”)
12
Since the number of occupied apartments were known, the peak-hour A.M. and P.M.
volumes were relatively easier to compute than the previous two steps. After collecting
and analyzing the data from both ITE and observed method, the results were compared to
discover the impact, if any, of BART proximity. The data comparison procedure is shown
in the next step.
Data Comparison
After finishing the previous two steps, it was necessary to compare the trips with
the ITE suggested results. Since all selected TOAs are three to five stories high, their data
was compared with the mid-rise apartments, provided in the “Land Use 223” of the Trip
Generation Manual (ITE). The Trip Generation Manual, which is published by the
Institute of Transportation Engineers, estimates the number of trips that a specific land
use will produce. It provides charts and equations to quickly estimate the number trips for
many land uses, including residential, industrial, agricultural, institutional, office, retail,
medical, and recreational. Transportation/Traffic Engineers generally refer to this manual
to evaluate the impact of development or redevelopment of land. Although it was
compiled after studying the trip generation behavior of many sites falling in each land
use, but it does not guarantee the accuracy since all sites behave differently. It is a
widely-used guideline, however. The data comparison results are provided and discussed
in detail in Tables 5 and 6, and in Figures 17 through 23 of Chapter 5 (Results and
Discussion).
13
Chapter 4
INFORMATION ABOUT SELECTED TOAs
For this project, ten TOAs near BART stations were chosen from all regions of
the BART network. The chosen TOAs were located on the Peninsula, and in the northern,
eastern, and southern parts of the East Bay. Only ten TOAs were studies due to limited
resources. The selected ten sites represented more than 22% of the possible BART
stations (44 station). The ease with which field data could be collected and retrieved from
the site played a significant role in the TOA selection. For example, apartment complexes
with more than four entrances and exits were not selected due to limited number of data
collectors; Archstone Walnut Creek near Pleasant Hill/Contra Costa Centre BART station
and Avalon Dublin Station near Dublin/Pleasanton BART station were not chosen
because both had more entrances and exits than could be managed with a limited number
of data collectors.
The accuracy of this project relied on the accuracy of the TOA site information,
including the total number of apartments, its occupancy rate, and the parking information.
Only those TOAs whose property managers agreed to provide the critical information (or
had it available on TOA’s website) were chosen. The walking distance from the
respective BART station also played a major role in selecting a TOA. The TOAs falling
within a half-mile radius were selected. The TOA selection criterion was discussed in
Chapter 3 (Methodology). The following sections provide detailed information about the
selected TOAs, and brief information about the cities in which selected TOAs are
located.
14
Union City
Union City is centrally located between Oakland and San Jose. It was
incorporated in 1959 and has diverse population of just over 70,000 (City of Union City,
2012). It falls in Alameda County and shares its boundaries with Hayward on the north,
Fremont on south, and Newark on west side (Google Maps, 2012). It is well connected
with the north, south and Peninsula region of the San Francisco Bay Area through
highways and freeways, mainly I-880, I-580, I-680, CA-92, CA-84, and CA-238 (Google
Maps, 2012). The median household income of Union City is about 17% higher than
California’s average ($60,883 for Union City and $51,914 for California) (U.S. Census
Bureau, 2010). Also, the mean travel to work time is 30 minutes compared to California’s
average of 26.9 minutes (U.S. Census Bureau, 2010). There are two BART routes
(Fremont to Richmond and Fremont to Daly City) that serve the Union City BART
station. On a typical weekday, the daily BART ridership was more than 9,300 passengers
at Union City BART station (BART, 2012a). The passenger ridership information is
available for selected TOAs in Appendix C.
The TOA selected for this project was Avalon Union City located at 24 Union
Square in Union City. It was a five-story apartment complex with 443 units, including
studios, one-, two-, and three-bedroom apartments. In addition to the uncovered parking
spaces, the facility had one seven-story parking structure and a three-story underground
parking garage. The on-street metered parking was also available between the two
entrances/exits. The tenants were provided at least one parking space per unit; two for
select two- and three-bedroom units. The additional parking spaces could be rented for
15
$45 per month for uncovered parking and $60 per month for covered parking. The site’s
proximity to BART station separates it from other similar sites in the city; it was located
within a walk distance of three minutes from Union City BART station. It was also well
connected with other public transit agencies such as AC Transit, Dumbarton Express, and
Union City Transit. The Avalon Union City had an occupancy rate of 99% at the time of
field data collection. Figure 3 shows the general appearance and parking facilities (onand off-site) of Avalon Union City.
Figure 3. Union City – Avalon Apartments
16
Hayward
The city of Hayward is the third most populous city in Alameda County, after
Oakland and Fremont (U.S. Census Bureau, 2010). It is situated between Oakland and
San Jose, with Castro Valley and San Leandro on its north and Union City on its south
(Google Maps, 2012). It is home to the California State University (CSU), East Bay, and
Chabot Community College. The median household income and average travel time to
work of City of Hayward is almost same as that of the California’s average (U.S. Census
Bureau, 2010). There are two BART lines (Fremont to Richmond and Fremont to Daly
City) and two BART station stops in Hayward (South Hayward and Hayward). One TOA
was selected near each station. The daily weekday ridership at South Hayward and
Hayward BART station was more than 7,000 and 9,300 passengers, respectively (BART,
2012a).
Montelena Apartment Homes (MAH) was selected near the South Hayward
station and City Centre Apartment Homes near the Hayward station. MAH was located at
655 Tennyson Road in Hayward. It was conveniently located between CA-238 and South
Hayward BART station. This TOA was a three-story apartment complex with 189 oneand two-bedroom apartments. It had three vehicle access points, but only west side access
was used for both entering and exiting vehicles. One of the two accesses on the south side
was emergency access which was not used by residents, and the remaining access was
used only to exit the site. Although MAH was located adjacent to the BART station and
other transit facilities, but it was still classified as being “somewhat walkable,” due to
lack of other facilities within walking distance (Walk Score, 2012a). The tenants of MAH
17
were provided at least one parking space from the available covered and uncovered
parking spaces. Additional parking spaces were used by residents on first-come, firstserved basis. There was street parking available as well which was commonly used by
BART commuters. At the time of data collection, the occupancy rate was around 97%.
Figure 4 shows the general appearance and parking facilities (on- and off-site) of
Montelena Apartment Homes.
Figure 4. Hayward – Montelena Apartment Homes
The other Hayward TOA selected was City Centre Apartment Homes located
adjacent to the Hayward BART station. City Centre Apartment Homes were located at
18
22800 Meridian Drive in Hayward. It was few blocks from the City Hall, shopping
centers, services, and recreational facilities. This three-story apartment complex with
total 192 one-, two-, and three-bedroom apartments had occupancy rate of about 99% at
the time of field data collection. The site had personal garages, carports, and uncovered
parking available for tenants and each apartment was provided at least one parking space.
Extra parking spaces were used on first-come, first-served basis. There was street parking
available on the south side of the building as well, but was found unoccupied during the
data collection period. Figure 5 shows the general appearance and parking facilities (onand off-site) of City Centre Apartment Homes.
Figure 5. Hayward – City Centre Apartment Homes
19
Fremont
With population of about 217,000, the city of Fremont is the second most
populated city in Alameda County, after Oakland (U.S. Census Bureau, 2010). The city’s
median household income is about 1.5 times higher than California’s average, whereas
the mean commute time is slightly higher than California’s average (U.S. Census Bureau,
2010). It shares its borders with Milpitas, Union City and Newark (Google Maps, 2012).
There are two BART routes (Fremont to Richmond and Fremont to Daly City) that serve
the Fremont station. As of now, Fremont is the end of the BART line, but construction is
in progress to extend the line from Fremont to San Jose. On a typical weekday, the
average daily ridership was more than 17,000 passengers, almost half of which have
origin or destination of Downtown San Francisco (BART, 2012a).
The TOA selected in Fremont was Archstone Fremont, part of the famous
Archstone Apartment Communities located in more than twelve east and west coast
states. Archstone Fremont was situated at 39410 Civic Center Drive in Fremont. This site
was categorized as being “very walkable” (Walk Score, 2012a) and was located close to
BART, hospitals, shopping centers, recreational areas, and freeway (I-880) and highway
(CA-238). This four-story TOA had 328 one-, two-, and three-bedroom apartments with
garage and uncovered parking spaces. There were retail shops available at the ground
floor of the building, located right on the street. Each apartment was provided at least one
parking space; additional parking spaces could be taken on first-come, first-served basis.
In addition to the BART proximity, the site was also near the Santa Clara Valley
Transportation Authority (VTA) and AC Transit bus services. The occupancy rate was
20
about 99% at the time of the field data collection. Figure 6 shows the general appearance
and parking facilities (on- and off-site) of Archstone Fremont.
Figure 6. Fremont – Archstone Apartment Communities
21
Concord
The city of Concord is the most populous city of Contra Costa County (U.S.
Census Bureau, 2010). It is located about 35 miles northeast of San Francisco, 60 miles
north of San Jose, and 70 miles southwest of Sacramento (Google Maps, 2012). The
city’s median household income is about 7% higher and the mean commute time is 10%
higher than California’s average (U.S. Census Bureau, 2010). There is only one BART
route (Pittsburg/Bay Point to Millbrae) that serves the Concord station. The average daily
ridership of Concord BART station was more than 5,300 passengers (BART, 2012a).
The site selected in Concord was Park Central Apartments located at 1555
Galindo Street in Concord. It was located eight minutes by foot from the station. This
complex with 259 one-, two-, and three-bedroom apartments was categorized as being
“very walkable” (Walk Score, 2012a). The site had two vehicle entrances/exits, two
parking garages, and significant on-street parking which was frequently used by tenants.
Each apartment was provided at least one dedicated parking space, whereas additional
parking spaces were used by residents on first-come, first-served basis. At the time of
data collection, the site had an occupancy rate of about 99%. Figure 7 shows the general
appearance and parking facilities (on- and off-site) of Park Central Apartments.
22
Figure 7. Concord – Park Central Apartments
23
Colma
The town of Colma is located 10 miles south of San Francisco and about 42 miles
northwest of San Jose (Google Maps, 2012). At the Colma BART station, two routes
(Richmond to Millbrae and Pittsburg/Bay Point to Millbrae) serve the station. The
average daily BART ridership at Colma station was more than 9,200 passengers, almost
two-thirds of which is directed to/from Downtown San Francisco (BART, 2012a).
The TOA selected in Colma was La Terrazza Apartments situated at 7800 El
Camino Real in Colma. La Terrazza Apartments were located adjacent to the Colma
BART station. This apartment complex with garage parking and limited time street
parking had 153 one-, two-, and three-bedroom apartments. It also had few service shops
including a sandwich shop and tax services on the ground floor, facing the historic El
Camino Real which runs from San Jose to San Francisco. The site had one vehicle
entrance/exit but had multiple pedestrian doorways. The residents were provided at least
one parking space per apartment in the garage. The TOA had on-street parking as well
which was quite frequently by the residents. This TOA was considered to be “very
walkable” with a walk score of 72 (Walk Score, 2012a). At the time of field data
collection, the occupancy rate was about 96%. Figure 8 shows the general appearance
and parking facilities (on- and off-site) of La Terrazza.
24
Figure 8. Colma – La Terrazza Apartments
25
South San Francisco
With population over 64,000, this major city in San Mateo County is located on
the San Francisco Peninsula (U.S. Census Bureau, 2010), ten miles south of San
Francisco and about 40 miles northwest of San Jose (Google Maps, 2012). South San
Francisco’s median household income is about 22% higher than California’s average,
whereas mean travel time to work is 14% less than California’s average, probably due to
its proximity to San Francisco – a major employment generation hub (U.S. Census
Bureau, 2010). Two BART routes (Richmond to Millbrae and Pittsburg/Bay Point to
Millbrae) serve the South San Francisco BART station. The typical daily BART ridership
was more than 6,700 passengers, approximately two-thirds of which traveled between
Downtown San Francisco (BART, 2012d).
The site selected in South San Francisco was Archstone South San Francisco
situated at101 McLellan Drive in South San Francisco. This TOA was a 360 one- and
two-bedroom apartment complex. The building was located adjacent to the South San
Francisco BART station and on the historic El Camino Real. This four-story building had
two parking garages for residents, and some street parking was available near the site
which was found unoccupied on the data collection day. The residents were provided one
dedicated parking space, and the few extra spaces were used on the first-come, firstserved basis. It also had few service shops such as a coffee shop, bank, and wireless
phone store on the ground floor at street fronts. Figure 9 shows the general appearance
and parking facilities (on- and off-site) of Archstone South San Francisco.
26
Figure 9. South San Francisco – Archstone Apartment Communities
27
Walnut Creek
This city of Walnut Creek with population of 65,000 is located in the heart of
Contra Costa County (U.S. Census Bureau, 2010). It is about 25 miles northeast of San
Francisco and 47 miles north of San Jose (Google Maps, 2012). With median household
income of about $81,000, it is approximately 33% higher than California’s average;
whereas the mean travel time to work is almost same as that of California’s average (U.S.
Census Bureau, 2010). There is only one BART line (Pittsburg/Bay Point to Millbrae)
that serves the Walnut Creek station. The average daily ridership was about 13,500
passengers, half of which was between Downtown San Francisco (BART, 2012d).
The apartment complex selected in Walnut Creek was Trinity House Apartment
Homes, which was located at 1812 Trinity Avenue in Walnut Creek. It was situated
within a nine minute walking distance from the Walnut Creek BART station. This threestory TOA had 76 one- and two-bedroom apartments. The site was located at a walkable
distance from shopping, retail, service, entertainment, and schools, raising its walk score
to 82, which was second highest (after Downtown Berkeley’s Berkeleyan Apartments)
among selected ten sites (Walk Score, 2012a). The residents were provided at least one
dedicate parking space and additional parking spaces were used on first-come, firstserved basis. The site had on-street parking as well, which was quite frequently used. At
the time of field data collection, the occupancy rate was approximately 97%. Figure 10
shows the general appearance and parking facilities (on- and off-site) of Trinity House
Apartments.
28
Figure 10. Walnut Creek – Trinity House Apartment Homes
29
Berkeley
Home to renowned U.C. Berkeley and Lawrence Berkeley National Laboratory,
this East Bay city is located in Alameda County. It shares boundaries with Albany on the
north and Oakland and Emeryville on the south (Google Maps, 2012). With population of
about 114,000, the median household income is slightly less than California’s average,
however the number of people below poverty is significantly higher (18.9% for Berkeley
and 13.7% for California) (U.S. Census Bureau, 2010). From walkability perspective, the
city has the fourth highest walk score of 82 in California, after West Hollywood, Albany,
and San Francisco (Walk Score, 2012a).
There are two BART stations in Berkeley – North Berkeley and Downtown
Berkeley, so TOAs near both stations were selected. The selected Berkeley TOAs did not
provide any free or dedicated parking. Acton Apartments – a five-story, 71 one- and twobedroom apartments building was selected near North Berkeley BART station. It was
situated at 1370 University Avenue in Berkeley. On the ground floor, it had retail shops,
including a bakery/restaurant, salon, digital signs, and a self-defense training center. The
complex had limited parking and dedicated parking spaces were provided at a monthly
rate of $100 per parking space. There was on-street parking available but it was metered,
however it could be used for overnight parking after designated hours. This TOA was
considered to be “very walkable” with a walk score of 82 (Walk Score, 2012b). The site
had 100% occupancy at the time of field data collection. Figure 11 shows the general
appearance and parking facilities (on- and off-site) of Acton Apartments.
30
Figure 11. North Berkeley – Acton Apartments
The other apartment complex selected in Berkeley was Berkeleyan Apartments,
situated at 1910 Oxford Street in Berkeley. This complex was located in the heart of
downtown Berkeley, close to the Downtown Berkeley BART station. This five-story, 56
one- and two-bedroom apartment building had a café and office/retail space on the
ground floor. This TOA did not have any free parking, but it had limited garage parking,
which could be obtained for $150 per month per parking space. The site had limited onstreet parking. At the time of data collection, this TOA had 100% occupancy rate. It had
the best walk score (of 94) among all selected TOAs (Walk Score, 2012b). Figure 12
31
shows the general appearance and parking facilities (on- and off-site) of Berkeleyan
Apartments.
Figure 12. Downtown Berkeley – Berkeleyan Apartments
32
The size of selected TOAs varied from 56 for Downtown Berkeley to 443 for
Union City. The variation in the size of selected TOAs helped in observing the
relationship between the size of a TOA and the impact of BART proximity on trip
generation. Since the charts and equations provided by the Trip Generation Manual
relied on the number of occupied apartments, it was necessary to investigate the size and
occupancy rate of the selected TOAs. The number of apartments and their occupancy rate
for selected apartment complexes are shown in Figure 13. The occupancy rate of all
selected TOAs varies from 96% for Colma to 100% for North Berkeley and Downtown
Berkeley.
450
100%
99%
300
98%
225
97%
150
96%
75
0
95%
Figure 13. Size and Occupancy of Selected TOAs
Occupancy
Number of Occupied Apartments
375
33
The size of these selected TOAs – based on the total number of apartments –
varied from site to site, but all of these TOAs were three to five stories high. The
Downtown Berkeley (Berkeleyan Apartments) TOA was the smallest complex with just
56 apartments, whereas Union City (Avalon Apartments) was the largest with 443
apartments. The selected apartments were categorized under mid-rise apartments land
use, as suggested by the Trip Generation Manual (ITE). The mid-rise apartments (Land
Use Code 223) are those that are three to ten stories high (ITE). Using Google Maps, the
shortest walking distance and walk travel time between TOAs and the nearest BART
station were determined and are shown in Table 1. All selected TOAs were within onehalf mile radius of a BART station; the average walk time was less than six minutes. The
BART route map shows the selected TOAs in Figure 14.
34
Table 1. Distance Between Selected TOAs and the Nearest BART Stations
Selected TOAs
Nearest BART
Station
Approx.
Distance
(feet)
Approx.
Walk
Time
(minutes)
Union City – Avalon Apartments
Union City
550
3
South Hayward – Montelena Apartments
South Hayward
550
3
Hayward – City Centre Apartments
Hayward
1,050
4
Fremont – Archstone Apartments
Fremont
2,100
7
Concord – Park Central
Concord
2,150
8
Colma – La Terrazza
Colma
550
3
South San Francisco – Archstone
Apartments
South San
Francisco
1,100
5
Walnut Creek – Trinity House Apartments
Walnut Creek
2,650
9
North Berkeley
1,600
7
Downtown
Berkeley
2,150
8
North Berkeley – Acton Courtyard
Apartments
Downtown Berkeley – Berkeleyan
Apartments
Note: Walk distances and times were estimated using Google Maps.
35
Figure 14. Selected TOA Locations (BART, 2012b)
Before initiating this project, it was necessary to investigate the BART ridership
at the selected BART stations to learn more about the nature (e.g., work, recreational) of
the BART trips. The monthly and yearly ridership data spreadsheets were reviewed and
compared for the selected BART stations to find any abnormalities in station usage. The
typical weekday ridership varied from 6,700 passengers for South San Francisco to
27,000 passengers for Downtown Berkeley (BART, 2012a). Figure 15 shows the
36
BART’s average daily ridership (total and to downtown San Francisco) from selected
TOAs.
30,000
Downtown San Francisco
Total Ridership
Daily Ridership
25,000
20,000
15,000
10,000
5,000
-
Figure 15. BART’s Average Daily Ridership in September & October 2012 (BART,
2012a)
The selected TOAs were located very close, adjacent in few cases, to the BART
stations, so walkability was an essential feature of selected sites. Walkability, which has
health, environmental, and economic aspects, is guided by the absence or presence of
amenities, infrastructure, pedestrian safety laws, traffic conditions, and accessibility
(Walk Score, 2012c). To compare the walkability of the TOAs, walk score was used.
“Walk Score is a number between 0 and 100 that measures the walkability of any
37
address” (Walk Score, 2012c). A walk score based on many factors, including the type of
amenity (e.g., grocery store, restaurant, bank, theater), its distance from the points of
interest, pedestrian friendliness which is measured by the intersection density (number of
intersections per square mile) and average block length. Higher intersection density and
lower average block length are considered pedestrian friendly in computing walk score.
Points are assigned for each specific feature, total of which gives the walk score (Walk
Score, 2012d). The walkable neighborhoods reduce the commuting stress and increase
the community development. Also, people in walkable places are healthier, probably due
to the presence of amenities that facilitate or encourage walking (Walk Score, 2012c).
Walk score is divided into five categories, shown in Table 2, depending upon the number
of amenities present in the walking distance. New York City, which probably has one of
the highest population densities, ranks first with a walk score of 85.3 among the 50
largest U.S. cities. San Francisco and Boston rank second and third with walk scores of
84.9 and 79.2, respectively.
Walk score may be an important parameter that could potentially impact the
results of this project. Since employment related and shopping trips almost make up 50%
of the total trips produced by an average household (National Household Travel Survey,
2009), BART proximity and walkability to basic amenities could affect the number of
vehicle trips produced by selected TOAs. As a result, it was necessary to compare the
walk score of a TOA with the reduction, if any, of vehicle trips generated by each TOA.
38
Table 2. Walk Score Scale (Walk Score, 2012c)
Walk Score
90-100
Description
Walker's Paradise
Daily errands do not require a car.
70-89
Very Walkable
Most errands can be accomplished on foot.
50-69
Somewhat Walkable
Some amenities within walking distance
25-49
Car-Dependent
A few amenities within walking distance
0-24
Car-Dependent
Almost all errands require a car.
39
The walk scores of the TOAs and their cities are shown in Table 3. The
Downtown Berkeley TOA had the highest walk score of 94, whereas South San
Francisco had the lowest walk score of 57. Table 3 also shows the average walk score for
the city in which selected TOAs fall. The average walk scores of the TOA cities were in
the fifties or low sixties, with the exception of Berkeley which was in the eighties.
Table 3. Selected TOAs and Their City’s Walk Scores (Walk Score, 2012a; Walk Score,
2012b)
Location
TOA's Walk Score
City's Average Walk Score
Union City
66
52
South Hayward
60
58
Hayward
80
58
Fremont
75
52
Concord
88
53
Colma
72
Not Available
South San Francisco
57
62
Walnut Creek
88
51
North Berkeley
82
82
Downtown Berkeley
94
82
40
Chapter 5
RESULTS AND DISCUSSION
After analyzing the collected trip generation data and comparing it with the data
suggested in the ITE Trip Generation Manual (8th Edition), the impacts of BART
proximity on selected TOAs were clear. The results did vary, however, but the overall
assessment appeared to reduce the A.M. and P.M. peak-hour trips generated.
Table 4 shows the observed peak A.M. and P.M. peak-hour periods for all
selected TOAs. The A.M. peak hours varied from 7:00 a.m. to 9:00 a.m., as anticipated,
however the P.M. peak-hour varied from 5:00 p.m. to 7:15 p.m. for most TOAs, except
North Berkeley.
Table 4. A.M. and P.M. Peak-Hour Periods for Selected TOAs
TOAs
Peak A.M. Hour
Peak P.M. Hour
Union City
7:30 - 8:30
6:15 - 7:15
South Hayward
7:15 - 8:15
6:00 - 7:00
Hayward
7:00 - 8:00
6:00 - 7:00
Fremont
8:00 - 9:00
6:15 - 7:15
Concord
8:00 - 9:00
5:00 - 6:00
Colma
7:45 - 8:45
5:15 - 6:15
South San Francisco
7:30 - 8:30
5:45 - 6:45
Walnut Creek
7:00 - 8:00
5:45 - 6:45
North Berkeley
7:00 - 8:00
4:00 - 5:00
Downtown Berkeley
7:15 - 8:15
5:15 - 6:15
41
The first data analysis step was to find the peak A.M. and P.M. trip rate for all
selected sites and compare it with the Trip Generation Manual estimated trip generation
rate. Table 5 shows the A.M. and P.M. peak rates and percent difference between the
observed trip generation data and ITE-estimated trip data. Table 5 shows the A.M. and
P.M. observed and ITE-estimated number of trips generated by the specific TOA. It also
compares the observed and ITE trips for both the A.M. and P.M. The comparison shown
by a positive value represented the reduction in trip generation due to BART proximity
whereas a negative number represented the increase in trip generation. For instance,
Union City showed a 22% reduction in A.M. peak-hour trip generation, whereas Fremont
showed a 5% increase in P.M. peak-hour trip generation even though it is located very
close to BART station. The grahical representation of Table 5 for both A.M. and P.M.
periods are illustrated in Figures 16 and 17.
42
Table 5. Number of Observed A.M. and P.M. Peak-Hour Trips vs. ITE-Estimated PeakHour Trips
TOAs
Number of
Occupied
Dwelling
Units (DUs)
A.M.
PeakHour
Observed
Trips
Union City
439
146
188
22%
153
221
31%
Fremont
324
182
135
-35%
169
160
-5%
South
Hayward
184
71
71
-1%
67
86
22%
Hayward
190
57
73
22%
79
89
12%
Concord
257
62
104
41%
72
125
42%
Colma
147
48
54
10%
45
67
32%
354
86
149
42%
71
176
60%
76
25
21
-19%
24
29
17%
71
7
19
62%
6
26
77%
56
9
12
23%
7
18
62%
South San
Francisco
Walnut
Creek
North
Berkeley
Downtown
Berkeley
A.M.
A.M. %
P.M.
Peak- Difference
PeakHour
Hour
ITE
Observed
Trips
Trips
P.M.
P.M. %
Peak- Difference
Hour
ITE
Trips
43
Figures 16 and 17 depict the reduction in peak-hour trips for both A.M. and P.M.
for most of the TOAs. The size of the facilities did not influence the trip rate, as North
Berkeley (Acton Apartments), South San Francisco (Archstone Apartments), and
Concord (Park Central Apartments) showed maximum reduction, even though South San
Francisco and Concord are the second and fourth biggest TOAs, respectively, of the
project, whereas North Berkeley (Acton) was the smallest selected TOA. The Fremont
and Walnut Creek TOA showed higher number of trips, which was not anticipated, in the
A.M. peak-hour, however, Walnut Creek data showed reduction in P.M. peak-hour trips.
The South Hayward TOA showed neglible benefits of BART proximity in the A.M. time,
but improved in the P.M. time. Both Berkeley TOAs (Acton Apartments and Berkeleyan
Apartments) were the only two sites in the project which had no dedicated free parking
for the residents – only limited, paid parking was available, but North Berkeley TOA
performed better than the Berkeleyan Apartments even though it was located near less
walkable amenities, as shown by the Walk Score, compared to Berkeleyan Apartments.
44
Reduction in A.M. Peak-Hour Trips
80%
60%
40%
20%
0%
-20%
-40%
Figure 16. Percent Reduction in A.M. Peak-Hour Trips Compared with ITE
45
Reduction in P.M. Peak-Hour Trips
80%
60%
40%
20%
0%
-20%
Figure 17. Percent Reduction in P.M. Peak-Hour Trips Compared with ITE
After comparing the impact results shown in Figures 16 and 17 with the TOAs walk
scores, it was found that higher walk scores did not necessarily lead to higher reduction in
trips. For instance, the Fremont and Walnut Creek TOA had higher walk score than
Union City and South San Francisco TOA, but they generated higher number of trips.
The results did not conclusively suggest that a shorter distance between TOA and
San Francisco, which is a major employment generation hub and a BART ridership
46
attractor, lead to higher reduction in trip generation rate. However, the significantly
higher reduction in trip rate for Berkeley (North Berkeley and Downtown Berkeley) and
South San Francsico indicated higher reduction intrip generation rate, but Colma, which
to more closer to San Francisco than South San Francisco, did not strongly support it.
Also, Concord, which is quite farther than Berkeley and South San Francisco, showed
lower trip generation rate than Hayward or Union City, which are almost same distance
away from San Francisco.
The Peninsula cities – South San Francsico and Colma – performed well, but the
South San Francisco TOA resulted in significant reduction in peak-hour trips in both
A.M. and P.M. as compared to its neighbor Colma.
In the previous two graphs, the individual TOA results were discussed, but the
combined effect (as shown in Figures 18 and 19) of all TOAs clearly showed that the
BART proximity did help in reducing the trip generation rate in both A.M. and P.M.
hours. The solid line represents the observed trip generation data whereas the dashed line
stands for the ITE-estimated trip generation data. The lower slope of solid line evidently
demonstrates that the TOAs regardless of their size produce less number of peak-hour
trips than ordinary, away from transit apartment complexes. Also, the trendline equation
can be utilized to estimate the peak-hour trips for similar TOAs by simply inputting the
number of apartments in the equation. Also both solid and dashed line diverge, indicating
larger TOAs result in higher reduction in A.M. and P.M. peak-hour trips as compared to
smaller TOAs.
47
200
Observed
ITE
y = 0.46x - 14.01
Peak A.M. Trips
150
y = 0.37x - 9.36
100
50
0
0
100
200
300
400
Number of Dwelling Units (DUs)
Figure 18. Comparison of Observed A.M. Peak-Hour Trips with ITE
500
48
250
Observed
ITE
200
Peak P.M. Trips
y = 0.53x - 11.27
150
y = 0.37x - 7.82
100
50
0
0
50
100
150
200
250
300
350
400
450
500
Number of Dwelling Units (DUs)
Figure 19. Comparison of Observed P.M. Peak-Hour Trips with ITE
The graphs in Figures 18 and 19 show the impact of BART proximity on
individual project TOAs, without eliminating their size factor. But since all selected
TOAs do not have identical size, it is difficult to compare the results without graphing
them. The assignment of peak-hour trip rates for the number of dwelling units provides a
uniform platform to compare the results. Table 6 shows the observed and ITE-estimated
A.M. and P.M. peak-hour trip rate (or trips per dwelling unit).
49
Table 6. Observed and ITE-Estimated Peak-Hour A.M. and P.M. Trip Rate
TOA
A.M.
Observed
Trip Rate
A.M. ITE
Trip Rate
P.M.
Observed
Trip Rate
P.M. ITE
Trip Rate
Union City
0.33
0.43
0.35
0.50
Fremont
0.56
0.42
0.52
0.50
South Hayward
0.39
0.38
0.36
0.47
Hayward
0.30
0.39
0.42
0.47
Concord
0.24
0.41
0.28
0.49
Colma
0.33
0.36
0.31
0.45
South San
Francisco
0.24
0.42
0.20
0.50
Walnut Creek
0.33
0.28
0.32
0.38
North Berkeley
0.10
0.26
0.08
0.37
Downtown
Berkeley
0.16
0.21
0.13
0.33
Mean of All TOAs
0.30
0.36
0.30
0.45
Standard Deviation
0.13
0.08
0.13
0.06
The observed mean trip rates for TOAs for both A.M. and P.M. were less than the
mean trip rate estimated by ITE. The percent reduction in trip rate seems to be higher in
the P.M.; however the standard deviation, which represents variation of data from the
average, is also slightly higher in the P.M. compared to ITE, suggesting more data
variability. After conducting a t-test (95% confidence level) on the A.M. and P.M. peak-
50
hour trip rate to learn whether the difference between the observed and the ITE data were
statistically significant or not, it was found that the difference was not significant in the
morning (t-value = 1.222). However it was statistically significant in the afternoon (tvalue = 3.245). The two graphs (Figures 20 and 21) below show the graphical
representation of Table 6.
Downtown
Berkeley
A.M. ITE
North
Berkeley
A.M. Observed
Walnut
Creek
South San
Francisco
Colma
Concord
Hayward
South
Hayward
Fremont
Union City
0.00
0.10
0.20
0.30
Trip Rate
0.40
0.50
Figure 20. Comparison of Observed A.M. Peak-Hour Trip Rate with ITE
0.60
51
Downtown
Berkeley
P.M. ITE
P.M. Observed
North Berkeley
Walnut Creek
South San
Francisco
Colma
Concord
Hayward
South Hayward
Fremont
Union City
0.00
0.10
0.20
0.30
Trip Rate
0.40
0.50
0.60
Figure 21. Comparison of Observed P.M. Peak-Hour Trip Rate with ITE
Figure 22 shows the A.M. and P.M. peak-hour rate per dwelling unit for all TOAs
combined. The lower observed peak-hour trip both in A.M. and P.M. precisely shows the
positive impact of BART proximity by reducing the trip rates in comparison to the trip
rate provided by ITE manual.
52
P.M.
Peak
A.M.
Peak
0.00
0.10
0.20
0.30
0.40
Trip Rate
ITE Trip Rate
Actual Trip Rate
Figure 22. A.M. & P.M. Peak-Hour Trip Rate for All TOAs Combined
0.50
53
Chapter 6
CONCLUSION
In this project, the role of Transit-Oriented Apartments (TOAs) in reducing the
peak-hour A.M. and P.M. trip was observed. The selected ten TOAs were located near
five BART routes. The trip generation field data were collected and compared with the
Trip Generation Manual published by the Institute of Transportation Engineers (ITE) ,
which is used for traffic forecasting, to compute the benefits of BART proximity on
reducing personal vehicle trips.
After finishing the challenging task of selecting qualifying TOAs, the A.M. and
P.M. trip generation field data were noted, and analyzed to assess the trip reduction
impact, if any. Not only were the individual TOAs compared with the ITE manual’s
estimated results, the combined impact of all ten TOAs combined was compared.
The analysis suggested that the TOAs result in lower vehicle trip generation. The
two TOAs in Berkeley showed different performance, although both were almost same in
size and had no free or dedicated parking available, the North Berkeley TOA produced
fewer trips both in the morning and afternoon compared to Downtown Berkeley. The
collective results of all TOAs indicated the reduction in A.M. trips by 19% and P.M. trips
by 51%.
So the TOAs appear to be serving the purpose for which they were planned. They
may be reducing the congestion on the adjacent streets, possibly reducing the
infrastructure needs for peak vehicle traffic demand near BART stations. They may be
54
helping in reducing air pollution and greenhouse gases emissions, by providing people
opportunity to reside near BART system.
55
APPENDIX A
OBSERVED DATA
Field Data for Union City
Data collected on: Wednesday, 12 September 2012
Entering
Time
Interval
Exiting
A.M.
1st
Qtr.
Hour
a
2nd
Qtr.
Hour
b
3rd
Qtr.
Hour
c
4th
Qtr.
Hour
d
6:00 - 7:00
1
1
3
1
6:15 - 7:15
1
3
1
6:30 - 7:30
3
1
6:45 - 7:45
1
7:00 - 8:00
Total
1st
Qtr.
Hour
e
2nd
Qtr.
Hour
f
3rd
Qtr.
Hour
g
4th
Qtr.
Hour
h
6
10
7
15
10
42
48
4
9
7
15
10
17
49
58
4
3
11
15
10
17
25
67
78
4
3
7
15
10
17
25
31
83
98
4
3
7
4
18
17
25
31
26
99
117
7:15 - 8:15
3
7
4
4
18
25
31
26
36
118
136
7:30 - 8:30
7
4
4
14
29
31
26
36
24
117
146
7:45 - 8:45
4
4
14
7
29
26
36
24
22
108
137
8:00 - 9:00
4
14
7
3
28
36
24
22
20
102
130
8:15 - 9:15
14
7
3
9
33
24
22
20
16
82
115
8:30 - 9:30
7
3
9
0
19
22
20
16
0
58
77
4:00 - 5:00
11
18
17
22
68
7
9
9
8
33
101
4:15 - 5:15
18
17
22
19
76
9
9
8
5
31
107
4:30 - 5:30
17
22
19
22
80
9
8
5
8
30
110
4:45 - 5:45
22
19
22
18
81
8
5
8
17
38
119
5:00 - 6:00
19
22
18
16
75
5
8
17
12
42
117
5:15 - 6:15
22
18
16
21
77
8
17
12
12
49
126
5:30 - 6:30
18
16
21
28
83
17
12
12
15
56
139
5:45 - 6:45
16
21
28
20
85
12
12
15
15
54
139
6:00 - 7:00
21
28
20
21
90
12
15
15
18
60
150
6:15 - 7:15
28
20
21
24
93
15
15
18
12
60
153
6:30 - 7:30
20
21
24
23
88
15
18
12
13
58
146
A=
a+b+c+d
Total
B=
e+f+g+h
Total
Entering +
Exiting
A+B
P.M.
56
Field Data for South Hayward
Data collected on: Wednesday, 19 September 2012
Entering
Time
Interval
Exiting
A.M.
1st
Qtr.
Hour
a
2nd
Qtr.
Hour
b
3rd
Qtr.
Hour
c
4th
Qtr.
Hour
d
6:00 - 7:00
0
2
2
3
6:15 - 7:15
2
2
3
6:30 - 7:30
2
3
6:45 - 7:45
3
7:00 - 8:00
Total
1st
Qtr.
Hour
e
2nd
Qtr.
Hour
f
3rd
Qtr.
Hour
g
4th
Qtr.
Hour
h
7
2
5
4
6
17
24
1
8
5
4
6
7
22
30
1
2
8
4
6
7
11
28
36
1
2
3
9
6
7
11
21
45
54
1
2
3
8
14
7
11
21
11
50
64
7:15 - 8:15
2
3
8
3
16
11
21
11
12
55
71
7:30 - 8:30
3
8
3
3
17
21
11
12
5
49
66
7:45 - 8:45
8
3
3
6
20
11
12
5
7
35
55
8:00 - 9:00
3
3
6
1
13
12
5
7
3
27
40
8:15 - 9:15
3
6
1
1
11
5
7
3
1
16
27
8:30 - 9:30
6
1
1
0
8
7
3
1
0
11
19
4:00 - 5:00
6
5
5
4
20
2
7
2
4
15
35
4:15 - 5:15
5
5
4
14
28
7
2
4
4
17
45
4:30 - 5:30
5
4
14
8
31
2
4
4
6
16
47
4:45 - 5:45
4
14
8
9
35
4
4
6
6
20
55
5:00 - 6:00
14
8
9
6
37
4
6
6
3
19
56
5:15 - 6:15
8
9
6
13
36
6
6
3
6
21
57
5:30 - 6:30
9
6
13
13
41
6
3
6
6
21
62
5:45 - 6:45
6
13
13
8
40
3
6
6
6
21
61
6:00 - 7:00
13
13
8
11
45
6
6
6
4
22
67
6:15 - 7:15
13
8
11
11
43
6
6
4
3
19
62
6:30 - 7:30
8
11
11
12
42
6
4
3
2
15
57
A=
a+b+c+d
Total
B=
e+f+g+h
Total
Entering
+ Exiting
A+B
P.M.
57
Field Data for Hayward
Data collected on: Tuesday, 25 September 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
0
0
1
1
6
1
2
4
13
14
6:15 - 7:15
0
0
1
3
4
1
2
4
17
24
28
6:30 - 7:30
0
1
3
1
5
2
4
17
11
34
39
6:45 - 7:45
1
3
1
0
5
4
17
11
9
41
46
7:00 - 8:00
3
1
0
3
7
17
11
9
13
50
57
7:15 - 8:15
1
0
3
4
8
11
9
13
5
38
46
7:30 - 8:30
0
3
4
5
12
9
13
5
3
30
42
7:45 - 8:45
3
4
5
3
15
13
5
3
8
29
44
8:00 - 9:00
4
5
3
1
13
5
3
8
15
31
44
8:15 - 9:15
5
3
1
4
13
3
8
15
9
35
48
8:30 - 9:30
3
1
4
0
8
8
15
9
0
32
40
4:00 - 5:00
1
12
12
14
39
9
4
6
4
23
62
4:15 - 5:15
12
12
14
10
48
4
6
4
6
20
68
4:30 - 5:30
12
14
10
9
45
6
4
6
4
20
65
4:45 - 5:45
14
10
9
9
42
4
6
4
8
22
64
5:00 - 6:00
10
9
9
9
37
6
4
8
7
25
62
5:15 - 6:15
9
9
9
12
39
4
8
7
8
27
66
5:30 - 6:30
9
9
12
12
42
8
7
8
8
31
73
5:45 - 6:45
9
12
12
14
47
7
8
8
4
27
74
6:00 - 7:00
12
12
14
8
46
8
8
4
13
33
79
6:15 - 7:15
12
14
8
12
46
8
4
13
8
33
79
6:30 - 7:30
14
8
12
0
34
4
13
8
0
25
59
Total
Entering
+
Exiting
A+B
P.M.
58
Field Data for Fremont
Data collected on: Wednesday, 26 September 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
2
5
3
10
7
3
8
16
34
44
6:15 - 7:15
2
5
3
3
13
3
8
16
13
40
53
6:30 - 7:30
5
3
3
7
18
8
16
13
19
56
74
6:45 - 7:45
3
3
7
5
18
16
13
19
14
62
80
7:00 - 8:00
3
7
5
9
24
13
19
14
19
65
89
7:15 - 8:15
7
5
9
13
34
19
14
19
31
83
117
7:30 - 8:30
5
9
13
17
44
14
19
31
32
96
140
7:45 - 8:45
9
13
17
28
67
19
31
32
25
107
174
8:00 - 9:00
13
17
28
20
78
31
32
25
16
104
182
8:15 - 9:15
17
28
20
15
80
32
25
16
17
90
170
8:30 - 9:30
28
20
15
0
63
25
16
17
0
58
121
4:00 - 5:00
17
15
11
15
58
28
16
15
20
79
137
4:15 - 5:15
15
11
15
10
51
16
15
20
8
59
110
4:30 - 5:30
11
15
10
19
55
15
20
8
14
57
112
4:45 - 5:45
15
10
19
18
62
20
8
14
17
59
121
5:00 - 6:00
10
19
18
22
69
8
14
17
18
57
126
5:15 - 6:15
19
18
22
18
77
14
17
18
18
67
144
5:30 - 6:30
18
22
18
23
81
17
18
18
19
72
153
5:45 - 6:45
22
18
23
23
86
18
18
19
16
71
157
6:00 - 7:00
18
23
23
22
86
18
19
16
24
77
163
6:15 - 7:15
23
23
22
26
94
19
16
24
16
75
169
6:30 - 7:30
23
22
26
11
82
16
24
16
8
64
146
Total
Entering
+
Exiting
A+B
P.M.
59
Field Data for Concord
Data collected on: Wednesday, 3 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
1
1
0
1
3
1
5
16
14
36
39
6:15 - 7:15
1
0
1
1
3
5
16
14
11
46
49
6:30 - 7:30
0
1
1
0
2
16
14
11
13
54
56
6:45 - 7:45
1
1
0
1
3
14
11
13
11
49
52
7:00 - 8:00
1
0
1
3
5
11
13
11
7
42
47
7:15 - 8:15
0
1
3
4
8
13
11
7
15
46
54
7:30 - 8:30
1
3
4
3
11
11
7
15
12
45
56
7:45 - 8:45
3
4
3
3
13
7
15
12
14
48
61
8:00 - 9:00
4
3
3
3
13
15
12
14
8
49
62
8:15 - 9:15
3
3
3
3
12
12
14
8
9
43
55
8:30 - 9:30
3
3
3
0
9
14
8
9
31
40
4:00 - 5:00
6
5
10
7
28
2
3
9
2
16
44
4:15 - 5:15
5
10
7
15
37
3
9
2
7
21
58
4:30 - 5:30
10
7
15
9
41
9
2
7
7
25
66
4:45 - 5:45
7
15
9
8
39
2
7
7
5
21
60
5:00 - 6:00
15
9
8
14
46
7
7
5
7
26
72
5:15 - 6:15
9
8
14
10
41
7
5
7
6
25
66
5:30 - 6:30
8
14
10
15
47
5
7
6
5
23
70
5:45 - 6:45
14
10
15
7
46
7
6
5
6
24
70
6:00 - 7:00
10
15
7
8
40
6
5
6
4
21
61
6:15 - 7:15
15
7
8
9
39
5
6
4
5
20
59
6:30 - 7:30
7
8
9
0
24
6
4
5
0
15
39
Total
Entering
+
Exiting
A+B
P.M.
60
Field Data for Colma
Data collected on: Wednesday, 10 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
0
0
2
2
3
3
9
3
18
20
6:15 - 7:15
0
0
2
1
3
3
9
3
8
23
26
6:30 - 7:30
0
2
1
1
4
9
3
8
4
24
28
6:45 - 7:45
2
1
1
2
6
3
8
4
6
21
27
7:00 - 8:00
1
1
2
0
4
8
4
6
9
27
31
7:15 - 8:15
1
2
0
2
5
4
6
9
18
37
42
7:30 - 8:30
2
0
2
4
8
6
9
18
5
38
46
7:45 - 8:45
0
2
4
6
12
9
18
5
4
36
48
8:00 - 9:00
2
4
6
2
14
18
5
4
4
31
45
8:15 - 9:15
4
6
2
1
13
5
4
4
3
16
29
8:30 - 9:30
6
2
1
0
9
4
4
3
0
11
20
4:00 - 5:00
3
4
6
7
20
2
3
6
3
14
34
4:15 - 5:15
4
6
7
2
19
3
6
3
4
16
35
4:30 - 5:30
6
7
2
8
23
6
3
4
7
20
43
4:45 - 5:45
7
2
8
5
22
3
4
7
3
17
39
5:00 - 6:00
2
8
5
4
19
4
7
3
6
20
39
5:15 - 6:15
8
5
4
8
25
7
3
6
4
20
45
5:30 - 6:30
5
4
8
8
25
3
6
4
2
15
40
5:45 - 6:45
4
8
8
4
24
6
4
2
2
14
38
6:00 - 7:00
8
8
4
10
30
4
2
2
3
11
41
6:15 - 7:15
8
4
10
5
27
2
2
3
6
13
40
6:30 - 7:30
4
10
5
0
19
2
3
6
0
11
30
Total
Entering
+
Exiting
A+B
P.M.
61
Field Data for South San Francisco
Data collected on: Wednesday, 17 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
2
3
2
2
9
6
15
11
15
47
56
6:15 - 7:15
3
2
2
3
10
15
11
15
8
49
59
6:30 - 7:30
2
2
3
2
9
11
15
8
15
49
58
6:45 - 7:45
2
3
2
4
11
15
8
15
15
53
64
7:00 - 8:00
3
2
4
5
14
8
15
15
16
54
68
7:15 - 8:15
2
4
5
5
16
15
15
16
21
67
83
7:30 - 8:30
4
5
5
4
18
15
16
21
16
68
86
7:45 - 8:45
5
5
4
2
16
16
21
16
10
63
79
8:00 - 9:00
5
4
2
7
18
21
16
10
11
58
76
8:15 - 9:15
4
2
7
3
16
16
10
11
8
45
61
8:30 - 9:30
2
7
3
0
12
10
11
8
29
41
4:00 - 5:00
10
20
8
6
44
7
6
5
9
27
71
4:15 - 5:15
20
8
6
4
38
6
5
9
12
32
70
4:30 - 5:30
8
6
4
7
25
5
9
12
8
34
59
4:45 - 5:45
6
4
7
5
22
9
12
8
12
41
63
5:00 - 6:00
4
7
5
7
23
12
8
12
9
41
64
5:15 - 6:15
7
5
7
6
25
8
12
9
13
42
67
5:30 - 6:30
5
7
6
9
27
12
9
13
7
41
68
5:45 - 6:45
7
6
9
12
34
9
13
7
8
37
71
6:00 - 7:00
6
9
12
9
36
13
7
8
5
33
69
6:15 - 7:15
9
12
9
7
37
7
8
5
7
27
64
6:30 - 7:30
12
9
7
0
28
8
5
7
20
48
Total
Entering
+
Exiting
A+B
P.M.
62
Field A.M. Data for Walnut Creek
Data collected on: Thursday, 18 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
1
1
0
2
1
1
2
3
7
9
6:15 - 7:15
1
1
0
2
4
1
2
3
3
9
13
6:30 - 7:30
1
0
2
2
5
2
3
3
2
10
15
6:45 - 7:45
0
2
2
4
8
3
3
2
4
12
20
7:00 - 8:00
2
2
4
2
10
3
2
4
6
15
25
7:15 - 8:15
2
4
2
0
8
2
4
6
4
16
24
7:30 - 8:30
4
2
0
1
7
4
6
4
3
17
24
7:45 - 8:45
2
0
1
1
4
6
4
3
2
15
19
8:00 - 9:00
0
1
1
1
3
4
3
2
1
10
13
8:15 - 9:15
1
1
1
1
4
3
2
1
2
8
12
8:30 - 9:30
1
1
1
0
3
2
1
2
5
8
4:00 - 5:00
1
2
1
2
6
1
1
2
2
6
12
4:15 - 5:15
2
1
2
4
9
1
2
2
3
8
17
4:30 - 5:30
1
2
4
4
11
2
2
3
2
9
20
4:45 - 5:45
2
4
4
3
13
2
3
2
1
8
21
5:00 - 6:00
4
4
3
3
14
3
2
1
3
9
23
5:15 - 6:15
4
3
3
5
15
2
1
3
2
8
23
5:30 - 6:30
3
3
5
3
14
1
3
2
3
9
23
5:45 - 6:45
3
5
3
3
14
3
2
3
2
10
24
6:00 - 7:00
5
3
3
3
14
2
3
2
2
9
23
6:15 - 7:15
3
3
3
2
11
3
2
2
1
8
19
6:30 - 7:30
3
3
2
1
9
2
2
1
1
6
15
Total
Entering
+
Exiting
A+B
P.M.
63
Field Data for North Berkeley
Data collected on: Tuesday, 30 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
0
0
0
0
0
0
0
0
0
0
6:15 - 7:15
0
0
0
1
1
0
0
0
1
1
2
6:30 - 7:30
0
0
1
0
1
0
0
1
0
1
2
6:45 - 7:45
0
1
0
0
1
0
1
0
2
3
4
7:00 - 8:00
1
0
0
0
1
1
0
2
3
6
7
7:15 - 8:15
0
0
0
0
0
0
2
3
1
6
6
7:30 - 8:30
0
0
0
0
0
2
3
1
1
7
7
7:45 - 8:45
0
0
0
1
1
3
1
1
0
5
6
8:00 - 9:00
0
0
1
0
1
1
1
0
0
2
3
8:15 - 9:15
0
1
0
0
1
1
0
0
1
2
3
8:30 - 9:30
1
0
0
0
1
0
0
1
0
1
2
4:00 - 5:00
0
2
1
0
3
1
2
0
0
3
6
4:15 - 5:15
2
1
0
0
3
2
0
0
0
2
5
4:30 - 5:30
1
0
0
1
2
0
0
0
0
0
2
4:45 - 5:45
0
0
1
1
2
0
0
0
0
0
2
5:00 - 6:00
0
1
1
1
3
0
0
0
0
0
3
5:15 - 6:15
1
1
1
0
3
0
0
0
0
0
3
5:30 - 6:30
1
1
0
0
2
0
0
0
1
1
3
5:45 - 6:45
1
0
0
1
2
0
0
1
1
2
4
6:00 - 7:00
0
0
1
1
2
0
1
1
0
2
4
6:15 - 7:15
0
1
1
0
2
1
1
0
0
2
4
6:30 - 7:30
1
1
0
0
2
1
0
0
0
1
3
Total
Entering
+
Exiting
A+B
P.M.
64
Field Data for Downtown Berkeley
Data collected on: Tuesday, 30 October 2012
Entering
Exiting
Time
Interval
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
1st
Qtr.
Hour
2nd
Qtr.
Hour
3rd
Qtr.
Hour
4th
Qtr.
Hour
Total
A.M.
a
b
c
d
A=
a+b+c+d
e
f
g
h
B=
e+f+g+h
6:00 - 7:00
0
1
0
1
2
0
0
1
0
1
3
6:15 - 7:15
1
0
1
0
2
0
1
0
0
1
3
6:30 - 7:30
0
1
0
2
3
1
0
0
2
3
6
6:45 - 7:45
1
0
2
2
5
0
0
2
1
3
8
7:00 - 8:00
0
2
2
0
4
0
2
1
1
4
8
7:15 - 8:15
2
2
0
0
4
2
1
1
1
5
9
7:30 - 8:30
2
0
0
0
2
1
1
1
0
3
5
7:45 - 8:45
0
0
0
2
2
1
1
0
0
2
4
8:00 - 9:00
0
0
2
1
3
1
0
0
0
1
4
8:15 - 9:15
0
2
1
0
3
0
0
0
0
0
3
8:30 - 9:30
2
1
0
0
3
0
0
0
0
0
3
4:00 - 5:00
0
1
0
0
1
0
0
1
0
1
2
4:15 - 5:15
1
0
0
1
2
0
1
0
0
1
3
4:30 - 5:30
0
0
1
2
3
1
0
0
2
3
6
4:45 - 5:45
0
1
2
0
3
0
0
2
1
3
6
5:00 - 6:00
1
2
0
0
3
0
2
1
0
3
6
5:15 - 6:15
2
0
0
2
4
2
1
0
0
3
7
5:30 - 6:30
0
0
2
0
2
1
0
0
1
2
4
5:45 - 6:45
0
2
0
1
3
0
0
1
0
1
4
6:00 - 7:00
2
0
1
0
3
0
1
0
1
2
5
6:15 - 7:15
0
1
0
1
2
1
0
1
0
2
4
6:30 - 7:30
1
0
1
0
2
0
1
0
0
1
3
Total
Entering
+
Exiting
A+B
P.M.
65
APPENDIX B
TRIP RATE FOR MID-RISE APARTMENTS
Average A.M. Peak Hour Vehicle Trips (ITE)
66
Average P.M. Peak Hour Vehicle Trips (ITE)
67
APPENDIX C
BART RIDERSHIP
Weekday Ridership in September 2012 (BART, 2012a)
BART Station
Ridership between
Station &
Downtown San
Francisco
Total Ridership
Downtown SF %
of Total Ridership
North Berkeley
4,301
8,977
48%
Berkeley
7,428
27,112
27%
Hayward
3,035
10,234
30%
South Hayward
2,691
7,001
38%
Union City
4,129
9,122
45%
Fremont
7,906
16,752
47%
Concord
5,185
12,115
43%
Walnut Creek
6,864
13,360
51%
Colma
5,915
9,052
65%
South San
Francisco
4,252
6,566
65%
51,706
120,292
43%
Total
68
Weekday Ridership in October 2012 (BART, 2012a)
BART Station
Ridership between
Station &
Downtown San
Francisco
Total Ridership
Downtown SF %
of Total Ridership
North Berkeley
4,495
9,193
49%
Berkeley
7,511
27,020
28%
Hayward
3,230
11,402
28%
South Hayward
2,766
7,143
39%
Union City
4,356
9,532
46%
Fremont
8,399
17,475
48%
Concord
5,504
12,502
44%
Walnut Creek
7,126
13,657
52%
Colma
6,278
9,398
67%
South San
Francisco
4,552
6,926
66%
54,218
124,250
44%
Total
69
Average Weekday Ridership in September 2012 and October 2012 (BART, 2012a)
BART Station
Ridership between
Station &
Downtown San
Francisco
Total Ridership
Downtown SF %
of Total Ridership
North Berkeley
4,398
9,085
48%
Berkeley
7,469
27,066
28%
Hayward
3,133
10,818
29%
South Hayward
2,728
7,072
39%
Union City
4,242
9,327
45%
Fremont
8,152
17,114
48%
Concord
5,344
12,309
43%
Walnut Creek
6,995
13,509
52%
Colma
6,097
9,225
66%
South San
Francisco
4,402
6,746
65%
52,962
122,271
43%
Total
70
REFERENCES
1.
American Public Transportation Association (2012). Ridership Report (Second
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http://www.apta.com/resources/statistics/Documents/Ridership/2012-q2-ridershipAPTA.pdf
2.
BART (2012a). Monthly Ridership Reports. September 2012 & October 2012.
Accessed 8 November 2012 at http://bart.gov/about/reports/ridership.aspx
3.
BART (2012b). Station List. Accessed 8 November 2012 at
http://bart.gov/stations/index.aspx
4.
Belzer, D., Srivastava, S., Wood, J., & Greenberg, E. (2011, May). TransitOriented Development (TOD) and Employment. Accessed 3 November 2012 at
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5.
City of Union City (2012). About Union City: Facts & Figures. Accessed 24
October 2012 at http://www.ci.union-city.ca.us/about_uc.html
6.
CTOD (Center For Transit-Oriented Development) 2012a. Frequently Asked
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7.
Google Maps (2012). Accessed 5 November 2012 at https://maps.google.com/
8.
Inflation Calculator (2012). US Inflation Calculator. Accessed 5 November 2012
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9.
ITE (Institute of Transportation Engineers). Trip Generation Manual (8th Edition)
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71
10.
National Household Travel Survey (2009). Summary of Travel Trends. Accessed
26 November 2012 at http://nhts.ornl.gov/2009/pub/stt.pdf
11.
Reconnecting America (2004a). Why Transit-Oriented Development And Why
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http://ctod.org/pdfs/tod101.pdf
12.
Reconnecting America (2004b). Hidden In Plain Sight - Capturing The Demand
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13.
Reconnecting America (2008). Center for TOD Demand Estimate Update. (2008,
February 13). Accessed 4 November 2012 at
http://www.reconnectingamerica.org/resource-center/books-andreports/2008/center-for-tod-demand-estimate-update/
14.
U.S. Census Bureau (2010). State & County QuickFacts. Accessed 1 November
2012 at http://quickfacts.census.gov/qfd/states/06000.html
15.
U.S. GAO (United States Government Accountability Office) (2009). Affordable
Housing In Transit-Oriented Development. Accessed 26 November 2012 at
http://www.gao.gov/new.items/d09871.pdf
16.
Walk Score (2012a). Get Your Score. Accessed 1 November 2012 at
http://www.walkscore.com/
17.
Walk Score (2012b). Cities in California. Accessed 1 November 2012 at
http://www.walkscore.com/CA
72
18. Walk Score (2012c). What is walkability? Accessed 1 November 2012 at
http://www.walkscore.com/live-more/
19. Walk Score (2012d). Walk Score Methodology? Accessed 1 November 2012 at
http://www.walkscore.com/methodology.shtml