Analyzing the Growth Plan Vision: Innovations in Transportation Modelling

1. Analyzing the Growth Plan Vision: Innovations in Transportation Modelling Jesse Coleman, IBI Group 21st International EMME Conference Toronto, Ontario October 10, 2007

2. Outline • Introduction to GGH Model • Challenges • Land use typologies • Network development issues • Mode choice implications • Conclusions

3. Project Overview • Goal is to develop transportation and land use forecasting tools for the Ontario Ministry of Transportation (MTO) to be used for all major Ministry planning studies and environmental assessments (EA) • The model must be sensitive to Growth Plan land use changes and be able to capture the impacts of major public transit investments

4. Study Area Overview • The Growth Plan for the Greater Golden Horseshoe “Places to Grow” was created as a blueprint on how to accommodate new growth in the GGH. • Population projected to grow by 48% from 7.79 million in 2001 to 11.5 million in 2031 • Employment projected to grow by 46% from 3.81 million in 2001 to 5.56 million in 2031 • Covers a total land area of 33,400 sq. km.

5. Places to Grow • Allocate growth to built up areas where the capacity exists to best accommodate population and employment growth, while providing strict criteria for settlement boundary expansions • Promote transit supportive densities and a healthy mix of residential and employment land uses

6. Model Structure • Tour-based four stage model • 4 purposes: work, elementary/secondary school, post-secondary school, shopping, other • Auto ownership model (ordered logit) • Feedback between model elements for improved sensitivity (mode choice-trip distribution, trip-distribution-auto ownership) • Park and ride station choice model

7. Challenges • How to implement one model that can accurately predict travel behaviour in a very large geographic area, made up of several commuter sheds • Can one model handle this problem? • How to maximize sensitivity to land use policies and improvements in transit service, without hard-coding to current conditions Strategy: Solve challenges by focusing on micro scale network development issues and by basing all stages of the model around a land use area type typology

8. Land Use Classification

9. Land Use Area Type Classification • Area types are used to improve the model sensitivity to land use changes. • The area types feed directly into several model elements, including: • Network development • Auto ownership model • Trip distribution • Mode choice • Commercial vehicle trip generation • Several elements are incorporated into the classification: urban density, land use mix, road network configuration, and local nodes/corridors.

10. Area Type Density Classification

11. Area Type Density Classification

12. Area Type Land Use Mix Classification • An entropy measure is used to determine the land use mix, designated each zone as being either residential, industrial or mixed. • The land use mix classification is shown in the table below:

13. Network Development

14. Transit Walk Access • Problem • Need to remove zone size bias from the walk access/egress legs of transit trips • This effect is most severe outside the City of Toronto where zone sizes tend to be larger • Solution • Develop a means to derive actual walk distance from the network-coded straight-line distance from zone centroids to bus-stop nodes

15. Existing Transit Access Distances (TTS) A: Centroid Lengths B: Observed Transit Access Distances

16. Transit Walk Access • Walk access distance based on current centroid connectors is the MAXIMUM distance for a zone not the average Centroid Connector Zone Centroid Two Step Approach: • Apply factor to centroid length to obtain average straight line transit access distance • Apply a factor to convert from straight line to network distance

17. Transit Walk Access: Average Distance • For a typical zone the average walking distance is not represented by the existing centroid lengths: Straight Line Distance = 0.423 x Existing Centroid Length

18. Transit Walk Access: Network Distance • Pedestrian Route Directness (PRD) is a measure of the directness of a given path to a particular destination. • As nodes and corridors are developed within the land use, additional factors may be incorporated to reflect a shortening of walk distances in these areas

19. Transit Time Function • Need to accurately model transit travel times in different geographic areas to account for differences in stop spacing and dwell times • Approach • Bus travel time on a link/segment is a function of the run time and the dwell time (which in turn is affected by number of stops on the link) TTbus = [Average dwell time/stop]* [Number of stops] +  * [Auto travel time from assignment]

20. TTF Calibration • Input assumptions • Stop spacing by area-type • Effective stop spacing, based on frequency of bus stopping for passenger boarding/alighting • Average dwell time/stop • Area type is the main factor instead of operating agency

21. Results Total transit time vs auto time Transit run time vs auto time (run time+dwell time) (total time-dwell time) • Final transit time function = [DWTarea-type] * [ Length/STOP-SPCNGarea-type] + 1.1099 * AUTO-TIME

22. Transit Network Calibration • In addition to line count comparisons, analysis was completed to confirm that the GGH Model was replicating observed transferring behaviour • Initially, transfers were greatly over-predicted, with the biggest problems found replicating zero and one transfer trips. • The EMME disaggregate assignment feature was used to look at several case studies to identify where in the transit strategies transfers were being over-predicted. Two main problems were found: • Transfers being made for short one or two block transit trips at the access or egress end • Inconsistencies in definition of transit centroid connectors

23. Transit Network Calibration • Solution • Walk mode allowed on all links • Transfer/Boarding penalties increased • Ensured that all zones had centroid connectors joining to major arterials, and that this definition was consistent across all geographic areas. This fix led to significant improvements • There were some trip interchanges that were still not corrected using these measures due to zone size biases (i.e. differences in where people actually live within a zone and the location of the zone centroid)

24. Mode Choice Implications & Conclusions

25. Work Tour Mode Choice • Nested Logit mode choice models have been estimated using all of the land use variables based on the improved network sensitivities • Strong land use variables, no region/city specific dummy variables to limit long term policy sensitivity. • Model predicts well across all regions, confirming that one model will be sufficient for the whole GGH • Some “regression to the mean” issues to resolve • Land use variables do not compromise sensitivity of level of service variables

26. Conclusions and Future Work • Detailed network calibration exercises ensure an accurate portrayal of the mode choice decisions being made, improving the sensitivity of the model to level of service changes. • Using a land use area type system allows degrees of freedom to calibrate model to different land use types and cities/regions without hard coding current behaviour by using region/city-specific dummy variables.