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

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

2 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

3 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.

4 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

5 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

6 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

Land Use Classification

8 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.

9 Area Type Density Classification Density Range (People+Jobs/Hect are) Land Use TypeTransit Level of Service <10RuralUnable to support transit service Suburban Low DensityUnable to support minimum level of bus service (30 minute headways). Opportunity for limited dial-a-bus service Suburban High DensityMinimal bus service, operating at 30 minute headways Urban Low DensityIntermediate bus service (10-20 minute headways) Urban High DensityFrequent Bus Service (less than 10 minute headways). At the upper end of the range, can support some higher order transit (BRT/LRT) if linking high density centres. 200+CBDSupports higher order transit such as BRT /LRT, ideally in high density nodes connected by medium/high density corridors. High capacity rapid transit modes such as subways can be supported when densities exceed 400 people+jobs per hectare.

10 Area Type Density Classification

11 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. Jobs/Workers Entropy Measure Jobs/WorkersLand Use Type <0.85Workers>JobsResidential <0.85Jobs>WorkersIndustrial >0.85n/aMixed The land use mix classification is shown in the table below:

Network Development

13 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

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

15 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

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

17 Transit Walk Access: Network Distance Pedestrian Route Directness (PRD) is a measure of the directness of a given path to a particular destination. Neighbourhood TypePRD Ratio Value (Hess 1997, Randall and Baetz 2001) Urban : Grid street patterns, streetcar suburbs, pre-1940s neighbourhoods 1.3 Suburban : Curvilinear street patterns, cul-de-sacs, conventional suburbs, postwar 1.7 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

18 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) TT bus = [Average dwell time/stop]* [Number of stops] +  * [Auto travel time from assignment]

19 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

20 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 = [DWT area-type ] * [ Length/STOP-SPCNG area-type ] * AUTO-TIME

21 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

22 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)

Mode Choice Implications & Conclusions

24 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

25 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.