Technical Advisory Committee June 3th, 2015

Today’s Agenda Work plan progress report. GTAModel V4.0 update Surface transit speed updating GTAModel V4.0 Workshop Other Business Adjournment

Work Plan

Work Plan Generally on-schedule: Multi-class auto assignment done. HOV network coding done. V4 documentation complete by mid June. Economic evaluation & visualization in good shape but development will continue.

Work Plan On-target for next quarter: On-target for future year network DBMS, 3-step freight model, XTMF Core 1.1 Preliminary multi-class, congested transit assignment testing begun. Active transportation mode choice model underway.

GTAModel V4.0 Model Overview Mode Choice Parameter Results Mode Choice Results Transit Parameter Results Boardings Screenlines

Model Overview

Mode Choice Parameter Results Value of Time P G S M St U Auto 29.9867159 27.60404 29.65409 29.97654 29.92864 28.20381 Transit 15.5018972 27.91662 13.38866 29.99561 26.36896 15.83214 walk/ivtt P G S M St U Transit 2.82246223 3.117891 3.197698 1.66546 3.093211 2.659376 wait/ivtt P G S M St U Transit 3.57867404 2.44743 3.58938 2.143965 2.38517 3.237301

Mode Choice Results

Transit Parameter Results Value Congestion Penalty Perception 1 Congestion Exponent: TTF1 TTF2 TTF3 TTF4 TTF5 6.56 6.69 6.69 3.33 6.17 Fare Perception 15.14 Wait Time Perception 2.65 Toronto Walk Perception 1.35 Non-Toronto Walk Perception 1.16 Toronto Access Perception 1.82 Non-Toronto Access Perception 1.80 PD1 Walk Perception 1.12   Brampton Boarding Penalty 1.50 Durham Boarding Penalty 0.00457 GO Bus Boarding Penalty 7.50 GO Train Boarding Penalty 1.43 Halton Boarding Penalty 3.0 Hamilton Boarding Penalty 3.50 Streetcar (including Exclusive ROW) Boarding Penalty 4.29 Subway Boarding Penalty 0.95 TTC Bus Boarding Penalty 2.95 YRT Boarding Penalty 5.42 VIVA Boarding Penalty 2.79 MiWay Boarding Penalty 3.20 AM Assignment Period 2.04 PM Assignment Period 3.03

Predicted & Observed Boardings by Agency, AM & PM Peak Periods

Transfer Matrix, Predicted & Observed Boardings & Alightings, AM-Peak

TTC Station Activity by Time of Day

TTC Station Activity by Time of Day

Screenlines – Auto Counts SL CCDRS TTS Demand SL Description T1001 I 40,759 39,878 City West Boundary T1001 O 34,014 32,085 T1002 I 60,257 56,870 City North Boundary T1002 O 40,130 33,910 T1003 I 16,228 18,560 City East Boundary T1003 O 6,075 5,577 T1014 I 17,323 17,388 Central Area Cordon West T1014 O 11,375 7,625 T1058 I 7,879 6,376 Central Area Cordon North T1058 O 4,336 2,427 T1035 I 16,831 16,175 Central Area Cordon East T1035 O 12,208 6,820

Surface Transit Speed Updating High level objective: Develop a comprehensive model for the speed of transit vehicles which operate in mixed traffic. The calibration surface transit speeds will result in a model that more closely reflects existing conditions on the streets. Current lack of calibration/adjustment of surface speeds has a number of impacts on the model: It is difficult to assess the impact of future traffic conditions on transit services. Multimodal transportation planning is hindered without accurate assessment of transit performance. Surface transit modes may appear artificially more attractive to trip-makers, skewing mode splits.

Surface Transit Speed Updating Possible solutions: Segment level model for transit speeds, as a function of auto speeds, boardings, alightings, and other factors as determined. Land-use level model for transit speeds, which are a function of planning-level factors (such as daily traffic volumes, area type, adjacent land use, other factors as determined) Other?

Surface Transit Speed Updating Preliminary literature review findings: Thus far, there has been no literature found that discussed surface speed updating in a large regional model, like V4. Tangentially, surface speed updating is related to transit speed modeling, surface speed reliability, and transit vehicle dwell time modelling. Much of the work in surface speed updating is done via regression models. Feng et. al found bus speed was a function of: stop location, intersection delay, and traffic conditions (w.r.t time of day). Kieu et. al used multiple traffic data sources to relate bus speed and car speeds via a cross-validated regression model. Florida DOT found a linear relationship between bus and auto travel times, during a range of time periods. McKnight et. al had similar results to the above studies, but used proxies for traffic as time of day, or direction of travel. Overall, very limited work done in the field.

V4.0 Workshop Plan is to have a one-day workshop on GTAModel V4.0. Originally projected for July. July, however, will still be quite busy with City Modelling project & EJM will be away most of the month. Is August a feasible target for the workshop or will too many people be away on vacation?

Thank you! Next TAC Meeting: Wednesday, September 2, 2015, 10:00-12:00