Large-Scale Urban Travel Demand
Modelling In Alberta
JD Hunt, University of Calgary
AT Brownlee, City of Edmonton
DM Atkins, City of Calgary
17th Annual EMME/2 User Conference
Calgary AB, Canada
October 2003
Outline
Describe Modelling Systems in 2 Major Cities
• Background
• Model structure
• similar in 2 cities
• shared development
• Model implementation
• Issues
• Example results
• Conclusions
Background
Context and Motivations
• Alberta’s 2 major Cities
• Calgary CMA: 951,000 in 2001
• Edmonton CMA: 938,000 in 2001
• Transportation Master Plan Development
• Edmonton in 1999
• Calgary update upcoming
• Evaluate:
• trip flows for engineering design
• mobility benefits for planning
• environmental impacts for policy development
Background
Context and Motivations
Issue Category
Model Structure / Feature
 Design hour volumes
 all times of day
 safety
 emissions
 more trip purposes
 all personal travel & B/C
 all modes
 goods movements & B/C
 commercial travel
 transit use
 transit revenue
 economics of travel
 land use / transport
 land use / transport
 travel behaviour etc
 emissions / fuel consumption
 regional travel
 region
Background
Context
Hierarchy of models used:



Strategic - EMME/2 Based Regional
Travel Model (RTM)
Tactical – VISSIM & PARAMICS microsimulation Models
Operational - VISSIM micro-simulation
model; SYNCHRO signal model
Background
Hierarchical
Context
Modelling
Approach
Regional Travel Model
(EMME/2)
Strategic
VISSIM
Tactical / Corridor
SYNCHRO
Corridor / Route
Intersection
LEVEL OF DETAIL
(Network)
course
PROGRAM LEVEL
fine
MODEL TYPE
TRAVEL DEMAND
AREA OF
COVERAGE
Directly forecast
within model
City + Region
Forecast outside
model
Sub-area / Corridor
Forecast outside
model
Corridor / Intersection
Model Structure;
Basics
• Aggregate, Equilibrium approach
• EMME/2-based
• multi-class equilibrium assignment
• optimal strategies transit assignment
• Sizes
• Edmonton: 850 Zones, 11,800 road links
• Calgary: 1,447 zones, 14,000 road links
• All trips over 100 m by persons 5+ yrs old; typical weekday
• 25 travel segments
Edmonton Travel Model
City Of Edmonton
Strathcona County
Leduc County
Parkland County
Sturgeon County
Segment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Person Type
Elementary / Junior High Student
Elementary / Junior High Student
Elementary / Junior High Student
Elementary / Junior High Student
Elementary / Junior High Student
Senior High Student
Senior High Student
Senior High Student
Senior High Student
Senior High Student
Post-Secondary Student
Post-Secondary Student
Post-Secondary Student
Post-Secondary Student
Post-Secondary Student
Adult Worker (Not need car at work)
Adult
Adult Worker (Not need car at work)
Adult
Adult
Senior
Senior
Senior
Adult Worker (Need car at work)
Adult Worker (Need car at work)
Trip Purpose
Home to School
Home to Other Purpose
School to Home
Other Purpose to Home
Non-Home-Based
Home to School
Home to Other Purpose
School to Home
Other Purpose to Home
Non-Home-Based
Home to School
Home to Other Purpose
School to Home
Other Purpose to Home
Non-Home-Based
Home to Work
Home to Other Purpose
Work to Home
Other Purpose to Home
Non-Home-Based
Home to Other Purpose
Other Purpose to Home
Non-Home-Based
Home to Work
Work to Home
Model Structure;
Basics
• Nested logit above assignment
• More than ‘4 steps’
• elastic generation
• destination choice
• time of day choice
• peak-spreading
• Complete feedback to generation
24 Hour Trip Generation:
p e rs o n i
0 tr i p
1 tr i p
....
1
8 tr i p s

24 Hour Trip Destination Choice:
o rig in z o n e i
1
d e s ti n a ti o n z o n e
j (1 )
j (2 )
....
2
j (n )

Time of Day Choice:
d a i l y i -j
2
am
o ff
3
pm
V

Mode Choice:
ti m e o f d a y i -j
m e ta b o l i c
m echanical
a u to
tr a n s i t
car
ca r1
ca r2
3
w alk
4
cy cle
p&r
5
6
ca r3
7
V

Peak Crown / Shoulder Choice:
c a r m o d e i -j
p e a k cro w n
7
peak s houlder
8
V
V
Model Structure;
Volume-Delay Functions
• Comparatvely steep volume-delay functions
• more ‘dimensions’ where demand can go
• Categories based on downstream conditions
• freeflow
• signal
• stop sign
• yield
Freeflow, capacity = 2000
Signals, capacity = 1000
Stop Signs capacity =1000
Yields, 2 different ones
Model Structure;
Mode Alternatives
• Auto 1 person
• Auto 2 persons
• Auto 3+ persons
• Transit (bus + LRT) with walk access
• Transit with auto access (P+R), both directions
• Walk
• Bicycle
• School Bus (for school children)
Model Structure;
Time of Day Alternatives
• AM Peak (07:00-08:59)
• PM Peak (16:00-17:59)
• Offpeak (remainder of 24 hours)
• Peak Spreading for Auto Alternatives
• ½ hour AM Peak Head
• 1½ hour AM Peak Shoulder
• ½ hour PM Peak Head
• 1½ hour PM Peak Shoulder
½ hour head of peak
flow
1½ hour shoulders of peak
peak
time
Model Structure;
Mode Choice
• Nested logit
• Separate for each segment
Model Structure;
Mode Utility Functions
• Attributes
• Times and Costs from network assignment
• Transit headways not wait times (wait always ½ headway)
• Sector-to-Sector auto flows for 2 and 3+ Auto
• Full set of constants
• Bicycle function using SP data
• Estimation results generally good
• separate for each segment
• signs all OK
• ratios reasonable
• some constraining
Model Structure;
Destination Choice
• Singly-constrained
• at home end for home-based
• from home: destinations allocated
• to home: origins allocated
• at home end then origin end for non-home-based
• All zones for alternatives
For Home Based Segments:
home
&
trip
origin
home
&
trip
destination
trip destinations
trip origins
For Non-Home Based Segments:
home
trip
origin
trip destinations
trip
origin
trip destinations
trip
origin
trip destinations
Model Structure;
Destination Choice Utility Functions
• Attributes:
• Logsums from mode and time of day choice
• Attractor terms based
• population
• employment by type and for regional shopping centres
• hectares of park
• special generators: Airport, Stampede Casinos
• Intrazonal constants
• Sector-to-Sector constants
• Additional distance deterrence: a*distb
• Estimation results generally good
• Attractor term coefficients around 1
• Logsum term coefficients sometimes more than 1, held at 1
• Constants play large role
Model Structure;
Generation Choice
• Choice of trip frequency
• Alternatives are: 0, 1, 2, 3, etc trips
• Person-based
• household attributes included for person
• multiply by relevant population to get flow
• Single-level logit
Composite Utility
of Travel
0 trips
1)
1 trips
= ln Σ [e Utility for each number of trips alternative ]
2 trips
3 trips
...
8 trips
Utility fo r e a c h num b e r o f trip s a lte rna tive
= a * x * c o m p o site utility d e stina tio n c ho ic e + b * zo ne inc o m e + c
2)
Trip G e ne ra tio n (fo r a zo ne )
= num b e r o f p e o p le in zo ne * [(1 * Pro b (1 trip )) + (2 * Pro b (2 trip s)) + ... + (8 * Pro b (8 trip s))]
Model Structure;
Generation Choice Utility Functions
• Attributes:
• logsums from destination choice multiplied by trip frequency
• logsum for home-based
• weighted logsum for non-home-based
• zonal average income
• full set of constants
• Estimation results good
• signs all OK
• logsum term coefficients less than 1
• Logsums from generation choice provide consumer surplus
• change in logsum = change in consumer surplus
• convert to $ equivalents
Model Structure;
Traveller Benefits Measure
• Logsums from generation choice provide consumer surplus
• change in logsum = change in consumer surplus
• convert to $ equivalents
• Basis for traveller benefits of transportation policy
• Requires stricter convergence criteria
Model Structure;
Consumer Surplus
price
quantity
Model Structure;
Consumer Surplus
price
generalized
cost
quantity
Model Structure;
Consumer Surplus
price
generalized
cost
pw
Δq
quantity
Model Structure;
Consumer Surplus
price
generalized
cost
pw
p1
Δq
quantity
Model Structure;
Consumer Surplus
price
generalized
cost
p1
q1
quantity
Model Structure;
Consumer Surplus
price
generalized
cost
change in consumer surplus
for change from p1 to p2 (a loss)
p2
p1
q2
q1
quantity
Model Structure;
Emissions Model
• Post-processor using EMME/2 link output for each time period
• flow volumes by vehicle type
• travel times & delays
• Driving cycle simulated for each link:
• traffic motion trajectory, showing cruise, acceleraton,
deceleration and stop patterns.
• Leads to for each link: second by second vehicle tractive power
requirements, fuel consumption, and pollutant emissions.
• Vehicle fleet / emission profiles in 5-year increments to 2020
• Currently determines link quantities for:
• CO2, CO, NOX, Reactive Hydrocarbons, Fuel Consumption
• Model being updated for MOBILE6 emissions, particulates
Model Implementation
• EMME/2 9.0:
• 5 databanks
• used to be 99 matrices in each, now more …
• 3 Pentium 1.8 GHz in parallel
• AM calculations, head and shoulder assignments
• PM calculations, head and shoulder assignments
• OFFPEAK calculations, generation and distribution
and single offpeak assignment
• Iterative loops within nesting structures
• feedback to all levels above assignment via logsums
• Convergence time
• Calgary: 36-48 hours
• Edmonton: 12-24 hours
Category
Population
Income
Car
Ownership
Employment
Special
Generators
Enrollment
School
Catchment
School Bus
Parking*
Transit Fares
Input Variable
# Total Population
# Elementary / Junior High Students
# Senior High Students
# Post-Secondary Students
# Adult Workers (Not needing car at work; not working at home)
# Adult Workers (Not needing car at work; working at home)
# Adult Workers (Needing car at work)
# Adult Other (non-institutional)
# Seniors (65+ who are retired)
Average Household Income
Average Cars /Person >15 years
# Retail Jobs (Total and Net of Work at Home)
# Regional Retail Jobs (Total and Net of Work at Home)
# Service Jobs (Total and Net of Work at Home)
# Hospital Service Jobs (Total and Net of Work at Home)
# Other Employment Jobs (Total and Net of Work at Home)
Stampede Park
Airport
# Hectares Park Space
# Elementary / Junior High School Enrolment
# Senior High School Enrolment
# Post-Secondary Enrolment
School Catchment Areas (Municipal level)
School Bus Eligibility
Average Daily / “Hourly” Parking Charges by Person Type
Average Parking Walking Distance
Zone-zone Transit Fares for each Person Type
Mode
Car
Transit
Transit
Transit
Transit
Walk
Walk
Walk
Bicycle
Bicycle
Bicycle
Road Type
Road Type
Road Type
Road Type
Road Type
Road Type
Road Type
Road Type
EMME/2 Mode
c
b
a
e
l
w
d
p
r
s
t
k
u
v
y
z
f
g
h
Description
car
bus (city diesel)
bus (region diesel)
bus (city trolley)
LRT
transit access to/from stop
walk mode in CBD
walk all the way
bicycle on road (in traffic)
bicycle on exclusive bicycle lane
bicycle on bicycle trail
freeway ramp
truck route
primary highway – city
arterial – city
collector – city
primary highway - region
arterial – region
collector – region
VDF
1
2
3
4
5
6
11
12
15
16
20
21
22
23
23
24
24
25
80
99
Control Type
Freeflow
Freeflow
Freeflow
Freeflow
Freeflow
Freeflow
Yield
Yield
Stop
Stop
Signal
Signal
Signal
Signal
Signal
Signal
Signal
Signal
Freeflow
Road Type
Freeway
Arterial/Rural Collector
Arterial
Arterial
Collector
Freeway
Freeway ramp
Traffic circle
Arterial/Collector
Collector
Arterial
Major Arterial
Arterial
Arterial
Arterial
Collector
Arterial/Collector
Arterial/Collector
Bridge
Zone Connector
Location
All
All
Special
Special
All
All
All
All
All
All
Special
CBD
Suburban
Inner City
CBD
Suburban
CBD
CBD
All
Capacity/Lane
1,900
1,750
1,500
1,300
700
2,000
1,250
900
800
700
1,250
1,150
1,050
900
900
700
700
500
See Table 13b
9,999
Initial seed trip tables
A
Calgary RTM Model Flow
ASSIGNMENT - get times &
costs
Get TRAVEL UTILITIES for
each segment / mode / time of
day
Get daily TRIP
DESTINATION
ATTRACTIVENESS
Get
DAILY COMPOSITE
UTILITY
Get DAILY COMPOSITE
UTILITYof ACCESIBILITY
Get DAILY
PERSON TRIP
GENERATION
Calculate PERSON TRIP
TABLES for each segment
Calculate mode shares for
each segment / time of day
A
Calculate vehicle trips by
mode for all segments
B
Yes, do final
iteration
Veh TT converged?
BigMABS
No, calculate
new trip tables
~22h - 2 iterations
Get DAILYPERSON TRIP
DESTINATION choice
B
Final ASSIGNMENT all
trips (auto, transit, walk,
bike)
~4h - 3 iterations
ASSIGNMENT - get times &
costs
Get TRAVEL UTILITIES for
each segment / mode / time of
day
Calculate mode shares for
each segment / time of day
Calculate vehicle trips by
mode for all segments
Veh TT converged?
miniMABS
No, calculate
new trip tables
Yes, re - calculate
composite
utilities / trip
tables
Issues
• Why not?
• activity-based
• micro-simulation
• EMME/2 ‘comfort’
• development resource constraints
• variation in results vs central tendency
• risk
• staged process
• Not entirely estimated simultaneously
• practical considerations: tractability
• recognize some bias in standard errors
Issues
• Single money coefficient, but different levels in nest
• conversion of consumer surplus to ‘auto operating’ $
• Not pure nested logit
• logsum multiplied by number of trips for generation
• average logsum used for non-home-based distribution
• Development sequence
• estimation of utility function sensitivity coefficients
and nested logit forms
• calibration of utility function constants
• validation (and further adjustment)
• vehicle and transit screenlines
• transit fare elasticity
Example Results;
17 kms of new 2x2 lane expressway
• Consumer surplus up $46,000 per day
• 3,000 to 5,000 veh/h 2-way on expressway
• 300 new person trips per day all modes
• 1000 more auto trips per day
• VKT increased by 0.8%
• CO2 up by 36,000 kg per day
• NOX up by 90 kg per day
Example Results;
10 km extension of LRT line
• Consumer surplus up $87,000 per day all segments
• up $35,000 per day for adults travelling to work
• 12,000 more transit trips per day
• 4,000 less auto trips per day (shift to P+R)
• 700 new person trips per day all modes
• CO2 down by 14,000 kg per day
• CO down by 600 kg per day
Example Results;
Widespread TDM
• TDM measures
• fuel taxes up
• parking charges up
• transit fares down
• Consumer surplus down $3,600,000 per day all segments
• 72% of this for working age adults
• 9% of this for seniors
• 8% of this for PSE
• 4% of this for 10-12 school children
• 7% of this for K-9 school children
Figure 12: Distribution of mobility benefits per day
in model results for 'extreme TDM' scenario
Aggregate Mobility Benefit
Mobility Benefit Per Person
population segment
aggregate mobility benefit
(1994 $)
0
Grade 10-12 Children
PSE Students
Working Age Adults
Senior Citizens
0.00
-500,000
-1.00
-1,000,000
-2.00
-1,500,000
-3.00
-2,000,000
-4.00
-2,500,000
-5.00
-3,000,000
-6.00
-3,500,000
-7.00
mobility benefit per person
(1994$)
Grade K-9 Children
Example Results;
Widespread TDM
• 111,000 fewer trips per day all modes (3% drop)
• 272,000 fewer auto trips per day
• increased trips per day:
•100,000 more transit trips
• 58,000 more walking trips
• 2,500 more bicycle trips
• VKT decreased by 16.4%
• roads with v/c > 0.95 in AM Peak: from 9.1% to 5.4%
• CO2 down by 830,000 kg per day
• Reactive hydrocarbons down by 4,100 kg per day
Conclusions;
Summary
• Practical modelling systems in 2 cities
• Important extensions to standard ‘4-steps’:
• explicit representation of peak-spreading
• elastic trip generation
• endogenous auto occupancy
• time of day choice
• consistent consumer surplus measure of benefits
• No external monitoring (or funds from outside Alberta)
Conclusions;
Observations
• EMME/2 provides fit-to-purpose tool
• facilitates development of modelling systems as intended
• framework for ‘storing’ knowledge about approaches
• Consumer surplus provides important insight
• help avoid focusing on ‘negatives’
• Transit or non-auto share more limited as indicator
• V/Cs > 1.05 only rarely
• steep volume-delay functions
• increased choice dimensions where demand can go
Conclusions;
Implications
• Potential for extending existing frameworks
• alternative to new approaches?
• comfortable software; EMME/2 important role
• lower risk, known principles
• still get very useful guidance in planning
• Important next stage in many instances …