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A Tour-Based Travel Demand Model for the Ottawa-Gatineau Region Part 1 – Travel Generation by P. Vovsha, V. Patterson, R. Donnelly, D. Stephens, P. Tremblay, D. Washnuk, L. Deneault Emme Users’ Conference, Toronto, October 2007. 2. Introduction. 3. Introduction. 1.2 M population. 4.

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Presentation Transcript
slide1

A

Tour-Based

Travel Demand Model

for the

Ottawa-Gatineau Region

Part 1 – Travel Generation

by P. Vovsha, V. Patterson,

R. Donnelly, D. Stephens, P. Tremblay, D. Washnuk, L. Deneault

Emme Users’ Conference, Toronto, October 2007

slide2

2

Introduction

slide3

3

Introduction

  • 1.2 M population
slide4

4

Introduction

  • 1.2 M population
slide5

for travel generation and spatial distribution

  • daily tours
  • for mode choice and traffic / transit assignments
  • both AM and PM trips

5

Major Structural Features

  • Advanced tour-based structure
  • draws on experience of first activity-based models
  • implementable in Emme in aggregate fashion
  • Conventional trip-based structure
  • other periods can be added in a future version
slide6

j

i

  • Tours (P-A)

j

i

2. Directional half-tours (O-D)

k

j

i

3. Chained trips (O-D)

k

6

Adopted Tour-Based Concept

  • Dealing with both tours and trips
slide7

consistency of time of day (TOD)-specific

trip matrices (AM and PM)

  • all TOD periods derived from

the same daily source

  • consistency between outbound and inbound

trip generation and distribution

7

Adopted Tour-Based Concept

  • Advantages taken
  • Advantages not taken
  • consistency of mode choice across TOD periods and outbound and inbound trips
slide8

8

Design of the Core Travel Model

slide9

9

Design of the Core Travel Model

Tour Generation

slide10

10

Design of the Core Travel Model

Tour / Trip Distribution

slide11

11

Design of the Core Travel Model

Trip Mode Choice

slide12

12

Population Synthesizer

slide13

External marginal controls for each traffic zone

  • household distribution by size
  • household distribution by housing type
  • total labour force in the zone
  • population distribution over 6 age ranges

13

Population Synthesizer

  • The only non-Emme component (JAVA)
  • List of 23,868 individual households in 556 traffic zones
  • IPF applied to the individual household weight from the O-D survey
  • Production of joint household distribution in each

traffic zone by 42 feasible combinations of:

  • 6 household size categories: 1, 2, 3, 4, 5, 6+
  • 4 household worker categories: 0, 1, 2, 3+
  • 2 housing types: 1=detached, 2=townhouses, apartments
slide14

14

Population Segmentation

Households are further allocated to 4 car sufficiency groups

Number of cars in household

Number of workers in

household

0

1

2

3+

0

Zero

High

High

High

1

Zero

Balanced

High

High

2

Zero

Low

Balanced

High

3+

Zero

Low

Low

Balanced

slide15

15

Population Segmentation

Population segmentation results in:

Sub-model Segment

Car Ownership

Tour Generation

Non-motorized

Time of DayChoice

Tour / Trip Distribution

Mode Choice

6 HH size

X

X

4 worker

X

X

2 housing

X

X

4 car sufficiency

X

X

X

X

X

Total

42

168

4

4

4

4

slide16

16

Travel Segmentation

  • 5 travel purposes
  • Work : workplace, work-related
  • School : high school,18 or younger
  • University : university, college / CEGEP,

other schools for 19+

  • Maintenance : shopping / banking, medical,

pick up / drop off

  • Discretionary : leisure / sport, eating out,

visiting relatives and friends

slide17

17

Travel Segmentation

Observed frequency by purpose

slide18

AM

PM

AM

6:30-8:59

AM

6:30-8:59

AM

AM

AM

AM

AM

PM

AM

AM

AM

PM

15:30-18:29

PM

PM

PM

PM

18

Travel Segmentation

  • Time of Day Correspondence

Early

Early

Early

4:00-6:29

Early

Early

Midday

Early

15 tour TOD combinations by outbound (→) & inbound (←) directions

Early

Late

5

trip

TODs

Midday

Midday

9:00-15:29

Late

Midday

Midday

Midday

Midday

Late

Late

18:29-28:00

Late

Late

Late

slide19

19

Travel Segmentation

Travel segmentation results in:

Sub-model Segment

Car Ownership

Tour Generation

Non-motorized

Time of DayChoice

Tour / Trip Distribution

Mode Choice

5 purposes

X

X

X

X

X

15 tour TODs

X

X

9 relevant

5 trip TODS

X

2 relevant

X

2 relevant

X

9 modes

Total

-

5

5

75

45

90

slide20

20

Combined Segmentation

Together, population and travel segmentations result in:

Sub-model Segment

Car Ownership

Tour Generation

Non-motorized

Time of DayChoice

Tour / Trip Distribution

Mode Choice

Population

42

168

4

4

4

4

-

5

5

75

45

90

Travel

42

840

20

300

180

360

Total

slide21

good enough for some models (generation)

  • more variables needed for others

(distribution, mode choice)

  • models applied to list of individual households, persons
  • unlimited segmentation / variables

21

Further Segmentation

Microsimulation ?

  • Conventional aggregate / zonal structure

limits segmentation :

  • 999 matrices is nearly not enough !
  • Individual microsimulation :
slide22

retail

  • service
  • public offices
  • private offices
  • education
  • health
  • industry
  • school
  • university
  • % low income
  • % detached houses

22

Land Use / Socio-Economic Data

  • Employment
  • Shopping
  • gross leasable area
  • Enrollment
  • Households
slide23

3 spatial levels tested statistically

  • 556 traffic zones
  • 94 super-zones
  • 26 districts
  • Measures
  • population density
  • employment density
  • retail employment density

23

Derivative Density Measures

slide24

24

Model Components

Tour Production

  • Household-based linear regression model
  • Segmented by 5 purposes,

42 HH compositions (HH size, # of workers, housing type)

and 4 car sufficiency groups

  • Includes derivative HH composition variables
  • # of non-workers with no worker (e.g. retirees, students)
  • # of non-workers with 1 worker (e.g. stay-at-home)
  • # of non-workers with 2+ workers (e.g. children)
  • Sensitive to density measures

at different spatial levels

slide25

Major variables

25

Model Components

Tour Attraction

  • Linear regression model
slide26

Binary logit choice model

  • motorized travel
  • non-motorized travel

26

Model Components

Pre-Mode Choice

  • Fully segmented by 5 purposes
  • Applied separately for HH daily tour productions

and zonal land use attractions

  • Production side segmented by

4 HH car sufficiency groups

slide27

27

Model Components

Time of Day Choice

  • Multinomial logit model with 15 TOD alternatives
  • Fully segmented by 5 purposes
  • Applied separately for HH tour productions

and zonal land use attractions

  • Production side :
  • extended to 60 alternatives by inclusion of

4 stop-frequency sub-alternatives

  • segmented by 4 car sufficiency groups
  • Attraction side :
  • driven by land use (employment) mix
  • sensitive to location / density measures
slide28

Work Half-Tours

28

Model Validation

slide29

University Half-Tours

29

Model Validation

slide30

30

Model Validation

School Half-Tours

slide31

31

Model Validation

Maintenance Half-Tours

slide32

32

Model Validation

Discretionary Half-Tours

slide33

33

To be continued …

slide34

A

Tour-Based

Travel Demand Model

for the

Ottawa-Gatineau Region

Part 2 –

Distribution and Mode Choice

by P. Vovsha, V. Patterson, D. Stephens, P. Tremblay

Emme Users’ Conference, Toronto, October 2007

slide35

2

Introduction

slide36

j

i

  • Tours (P-A)

j

i

2. Directional half-tours (O-D)

k

j

i

3. Chained trips (O-D)

k

3

Distribution

3 Steps of Matrix Construction

slide37

4

Distribution

Step 1 of Matrix Construction

  • Tour matrices in P-A format
  • Hybrid balancing-gravity model derived

from maximum entropy principle

  • Seed matrix prepared from the O-D survey

by “smoothing” (to avoid lumpiness)

slide38

O-D Survey

Impedance cij

Seed Matrix sij

Gravity Model

Proportional Balancing

Combined Model

5

Distribution

  • Hybrid Balancing – Gravity
slide39

4 steps of smoothing :

– aggregate O-D survey matrix to superzone level

– calculate traffic zone marginals

– calculate zone-to-zone gravity proportions

– redistribute aggregate matrix by gravity proportions within each superzone-to-superzone cell

  • 3 properties of a smoothed matrix :

– identical to O-D survey at superzone aggregation

– almost identical to O-D survey for zonal marginals

– smooth and logical at zone-to-zone level

6

Distribution

  • Matrix Smoothing
  • Matrices from O-D survey are “lumpy”

(expansion factor = 20) and cannot be used as

zone-to-zone seed matrices

slide40

Half-tour matrices in O-D format, by direction

  • outbound ( ij )
  • inbound / transposed ( ji )
  • Each direction processed by stop frequency
  • direct half-tours correspond to trips ( ij or ji )
  • half-tours with stops are broken into chained trips

( ik, kj or jk, ki )

7

Distribution

Steps 2 and 3 of Matrix Construction

slide41

Half-tour matrix :

Multinomial logit stop-location model :

Combined utility function

(based on both impedances

and stop attractions) :

3.23

3.21

3.21

1st trip leg matrix :

2nd trip leg matrix :

8

Distribution

  • Chained Trip Distribution
slide42

9

Mode Choice Estimation

Motorized Trips

slide43

10

Mode Choice Estimation

slide44

11

Mode Choice Estimation

slide45

12

Mode Choice Estimation

slide46

differential auto time coefficient

– free flow time

– congestion delay (unreliability effect)

13

Mode Choice Estimation

  • No separate bus and rail sub-nests

because of too few rail observations

  • Interesting and non-standard variables
  • Transitway / rail share of total transit distance,

as a reliability bonus

slide47

Willingness to Pay

14

Mode Choice Estimation

slide48

Transitway / Rail Reliability

15

Mode Choice Estimation

slide49

Transit time weights :

– walk : 1.2 (university, PM) – 2.8 (work, AM)

– wait : 2.2 (school, AM) – 3.4 (maintenance, AM)

– transfer, mins : 4.0 (work, PM) – 9.0 (work, AM)

  • Strong nesting

– substitution of transit modes

– limited substitution between auto driver and passenger

16

Mode Choice Estimation

  • Other Main Findings
  • Strong and consistent impact of car sufficiency

and density

  • Significant variation by purpose and somewhat

between AM and PM

slide50

Modelled explicitly with specific mode choice parameters

– P+R bus, K+R bus

– P+R rail, K+R rail

  • Trip matrices are broken by module 3.23 into

mode trip legs (auto / transit) for assignment

– AM : auto  transit

– PM : transit  auto

17

Mode Choice Estimation

  • Treatment of Bi-Modals
  • LOS skims are created using module 3.23,

with explicit identification of parking lots (all-or-nothing)

  • Parking lot capacity constraint can be introduced
slide51

18

Mode Choice Estimation

  • Assignable Trip Matrices

O-D trip mode

Traffic assign

Transit assign

Auto driver

X

Auto passenger

X

Walk to bus

X (auto leg)

X (bus leg)

P+R bus

X (bus leg)

K+R bus

X

Walk to rail

X (auto leg)

X (rail leg)

P+R rail

X (rail leg)

K+R rail

School bus

slide52

The TRANS model system is implemented as a 3-level macro

  • shell for batching (reconfigured for different projects)
  • 14 main models / procedures
  • multiple subroutines (like matrix “smoothing”,

chained trip distribution, etc.)

19

Main Conclusions

  • Emme is a convenient environment for implementation of an entire model system with advanced tour-based components
  • Limitations of the aggregate matrix-based paradigm with respect to model segmentation and variables
slide53

20

Future Enhancements

  • All time of day periods

for mode choice / assignments

  • Full daily equilibrium including distribution, mode choice and assignment
  • Peak spreading model (time of day

sensitive to congestion and pricing)

  • Gradual transition to microsimulation and activity-based structure
slide54

21

Thank you !