A Tour-Based Travel Demand Model for the Ottawa-Gatineau Region Part 1 – Travel Generation

1. 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. 2 Introduction

3. 3 Introduction • 1.2 M population

4. 4 Introduction • 1.2 M population

5. 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

6. 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

7. 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

8. 8 Design of the Core Travel Model

9. 9 Design of the Core Travel Model Tour Generation

10. 10 Design of the Core Travel Model Tour / Trip Distribution

11. 11 Design of the Core Travel Model Trip Mode Choice

12. 12 Population Synthesizer

13. 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

14. 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

15. 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

16. 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

17. 17 Travel Segmentation Observed frequency by purpose

18. 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

19. 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

20. 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

21. 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 :

22. 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

23. 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

24. 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

25. Major variables 25 Model Components Tour Attraction • Linear regression model

26. 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

27. 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

28. Work Half-Tours 28 Model Validation

29. University Half-Tours 29 Model Validation

30. 30 Model Validation School Half-Tours

31. 31 Model Validation Maintenance Half-Tours

32. 32 Model Validation Discretionary Half-Tours

33. 33 To be continued …

34. 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

35. 2 Introduction

36. 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

37. 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)

38. O-D Survey Impedance cij Seed Matrix sij Gravity Model Proportional Balancing Combined Model 5 Distribution • Hybrid Balancing – Gravity

39. 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

40. 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

41. 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

42. 9 Mode Choice Estimation Motorized Trips

43. 10 Mode Choice Estimation

44. 11 Mode Choice Estimation

45. 12 Mode Choice Estimation

46. 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

47. Willingness to Pay 14 Mode Choice Estimation

48. Transitway / Rail Reliability 15 Mode Choice Estimation

49. 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

50. 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