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DTA Applications Workshop

DTA Applications Workshop

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DTA Applications Workshop

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  1. DTA Applications Workshop Natalie Mendonca, Oakville, Canada

  2. Detail how we developed dynamic traffic models to suit the requirements of the assignment/project from a travel demand perspective Emphasis the challenges that were encountered And the solutions or innovation that was used in the development of these hybrid (meso- and micro-) simulation models or micro-simulation models Highlight three projects Introduction

  3. 1. Managed Lanes (MTO) Traffic/revenue forecasting for dynamically-priced managed lanes on the expressway 2. Event Management - 2015 Pan Am/ParaPan Am Games (MTO) Planning and evaluation of a network of temporary HOV lanes on the expressway system Development of traffic management plans to mitigate the impact of major incidents on the highways accommodating temporary HOV lanes 3. Re-Imaging Yonge Street (Sheppard Avenue to Finch Avenue) Environmental Assessment Study (City of Toronto) Assessment of the impacts when replacing a lane of traffic with cycling lanes on a busy urban corridor ThreeProjects

  4. 1. Managed Lanes (MTO) • Investigating potential conversion of existing HOV lanes into HOT lanes • QEW, 403, 404 • QEW HOTL Pilot Project • implemented in 2016 using existing HOV lanes • Monthly permits • Investigating viability of HOTL and revenue potential on Highway 427 • Feasibility and business-case assessment of HOTLs on 5 GTA highways • 400, 401, 404, 410, QEW • Highway 427 • MTO announced HOTLs will be operational by 2021 (under previous Government) 404 400 427 410 401 QEW

  5. Project overview – modelling framework

  6. Project overview What is dynamic pricing? Tolls are adjusted dynamically in response to changing real-time traffic demand and performance Tolls are set to maintain a specified performance level in the managed lane Typical dynamic pricing objective: Maintain a pre-determined target speed in the managed lane 90% of the time in the peak direction during the peak hour Maximize utilization of the managed lane subject to maintenance of the target speed 70 km/hr approximately consistent with 1,700 veh/hr Iterative search process: Simulate next 5 minutes into the future with current toll rates Increase or decrease toll rate in successive iterations until objective met or maximum allowable change in toll rate reached

  7. Required revenue projections during the day (15-hour period) and the macro model (GGHM v4) provided peak hour and peak period class-specific matrices (SOVs, HOV2, HOV3+, Light, Medium and Heavy Trucks) Thechallenge

  8. Matrix development for a 15-hour period

  9. The micro-simulation models were calibrated with respect to volume and performance metrics (speed and travel times) Model calibration at the corridor level

  10. Development of future 15-hour matrices at the corridor level 1. Peak hour growth ( AM and PM) Can directly add a % of the growth (depends on the calibration year) to the calibrated matrices 2. Off - peak growth • Made the assumption that the growth increments would follow the same volume distribution as the existing 15-hour period

  11. Development of future 15-hour matrices at the corridor level Matrices were generated by class for every hour in the 15-hour period: How was this done?

  12. Traffic performance measures • “Typical” traffic simulation outputs • Compliance with speed target • Travel time and reliability • Measures to support economic and financial analysis • Toll rates, revenue • Throughput, travel-time savings • Transactions (for estimation of back-office costs) • Vehicle trajectory files - input to SSAM and cost model for collision cost analysis Sample output from model

  13. Evaluate a network of temporary HOV lanes (on expressways) proposed for the Games to: • Assess extent of proposed network and eligibility policy (HOV2+ vs. HOV3+) • Assess ability of temporary HOVL network to reduce risk of not meeting required village-to-venue travel time targets for athletes/officials • Assess impact of network on day-to-day travel • Assess extent of TDM (reduction of auto travel in HOVL corridors during peak periods) required to maintain “business-as-usual” traffic conditions Client: Ministry of Transportation of Ontario WSP was assisted by CIMA Canada and AIMSUN 2. Event Management – 2015 Pan Am/ParaPan Am Games Study Objective

  14. Study area Network characteristics • 345 km (215 mi) expressway • 135 interchanges • 2,000 km (1,200 mi) surface streets • 920 traffic signals • 26,000 links Games characteristics • 30 venues distributed across/beyond the GTA • 7,700 athletes + officials • 1.2 million spectator tickets for sale Developed a large-scale hybrid model within Aimsunto cover the proposed temporary HOVL network, with the temporary HOVL corridors operating at the micro level

  15. 2011 travel demand scenario The background travel demand inputs were extracted from the 2011 Transportation Tomorrow Survey (TTS) These travel demand inputs were generated for 2:00 PM to 10:00 PM in 30-minute increments as the Games-related traffic and background traffic would be highest in the afternoons and evenings Matrices were developed for the Greater Toronto and Hamilton Area (GTHA)

  16. Volume/speed data at ~1,300 locations • Matrix adjustment and time-shifting (macro), check for operational issues • Driver behaviour/operation for HOVL corridors Calibration

  17. Travel demand inputs Base 2011 demand from TTS/GGHM (time-shifted) 2 pm to 10 pm in 30-minute slices SOV, HOV2, HOV3+, light/medium/heavy trucks Growth adjustment to 2015 Calibration/validation Seasonality (summer) adjustment • Spectator trips (time-shifted) and background trip offset Travel Demand Management trip reduction Games Client Group trips (athletes and officials) (time-shifted) Adjustments to auto occupancy to reflect eligibility criterion for temporary HOV lanes Venue workforce trips (time-shifted) Non-venue workforce trips (time-shifted) Layers “mixed and matched” to generate final demand matrices Assumed HOVL violations • In all, over 1,000 matrices were combined to create the final travel demand matrices.

  18. Not all O-D pairs are able to complete their trip within a 30-minute time slice and certain O-D pairs have target arrival and departure times that should be met Spectators, workforce and athletes had different arrival and departure times at the venues For example: time distribution assumptions for the spectators Arrival assumptions: 30% of the spectators arriving one-and-an-half hours prior to the event 50% of the spectators arriving one hour prior to the event 20% of the spectators arriving within half-an-hour before the event Departure assumptions: 70% leaving within half-an-hour after the event 30% leaving within one hour after the end of the event Thechallenge

  19. Time-shifting script A time-shifting script (API) was created within Aimsun that shifted the start time of the trip to account for its travel time to ensure that the vehicles reach their destination at their desired time.

  20. Support policy decisions: • HOV3+ before and during Pan Am Games (~3-4 weeks) – HOV2+ before and during ParaPan Am Games (~3-4 weeks) • Target 20% trip reduction on HOVL corridors during peak periods to (more-or-less) maintain “business as usual” Results: • 95% of trips by athletes/officials met travel time targets – no-one was late for their event • After a few days (before the Games) for “adjustment”, traffic generally moved without serious breakdown Outcomes– temporary HOVL network

  21. Objective of the study: 3. Re-Imaging Yonge Street (Sheppard Avenue to Finch Avenue) Environmental Assessment Study • To assess the traffic implications of reducing Yonge Street, between Sheppard and Finch Avenues, from 6 to 4 lanes with the curb lanes being converted to bicycle tracks (expanded to consider bicycle tracks on parallel service roads (Doris and Beecroft) • Surface street network implications • Implications for Highway 401 and its interchanges Client: City of Toronto

  22. Study area/focus area Developed a hybrid simulation model with the Yonge Street Corridor and as well as Beecroft + Doris operating in the micro-pocket of the model • Use micro within the focus area “pocket”: • Representation of pedestrians, bicycles, and transit • More detailed outputs • Visualization and video animation available Focus area • Use meso outside the focus area: • Need to consider alternative routing but don’t need detailed representation/outputs • Speeds up running of model significantly Study area

  23. Refine the zone system Within the focus area, subdivide traffic zones based on Statscandata available for “dissemination” areas – getting close to block level Thechallenge The traffic “zones” within the EMME macro model were relatively large and we needed traffic “zones” that are as fine as possible, to minimize distortion of local traffic patterns - ideally, each site access location/parking area would be a separate zone – but this is typically impractical

  24. Travel demand inputs • City of Toronto’s EMME model was used to generate subarea auto traversal matrices for 2011 and 2031 • Scenario-specific runs not available so all rerouting will be accounted for only within the study area EMME peak-hour auto matrices (City’s model) Adjust to refined zone system Growth increment matrix (existing to 2031) Calibrate existing peak-hour model – total vehicles Adjust to refined zone system) Add growth increment Subdivide into autos and medium/heavy trucks Develop for remaining hours of analysis period

  25. Transit • GO and TTC buses were represented according to their schedules • Detailed operations within micro pocket –eg. bus-bay, on-street, or station stops • (not represented at the mesolevel – bus volumes added to truck class) Pedestrians • Existing pedestrian volumes were coded at the intersections within the micro portion of the model (created pedestrian matrices at the intersections) • Pedestrian volumes were factored up for future scenarios based on population/employment growth for the area Cyclists • Existing bike volumes were modified/factored up for future scenarios to account for local trips that may convert to cycling trips once the new lanes were implemented

  26. Focused on 3 different projects and demonstrated how dynamic simulation models were developed to fit the scope of the assignment. The ML Assignment (MTO) - created a dynamic pricing API within Aimsun to model different pricing schemes and developed these Aimsun microsimulation models to operated for a 15-hour period Event Management – 2015 Pan Am/ParaPan Am Games (MTO) - created a dynamic hybrid model within Aimsun that allowed vehicles to maintain their desired arrival and departure times at venues Re-Imaging Yonge Street (Sheppard Avenue to Finch Avenue) Environmental Assessment Study (City of Toronto) – created an urban dynamic hybrid model within Aimsun that allowed vehicles to dynamically change their route based on modifications made to a major arterial while maintaining urban characteristics (pedestrians, cycling traffic, zones at the city block level, etc.) Conclusions

  27. Thank you!