1. 1 Assessing Travel Demand for Exclusive Truck Facilities Matthew Roorda, University of Toronto Michael Hain, University of Toronto Glareh Amirjamshidi, University of Toronto Rinaldo Cavalcante, University of Toronto Baher Abdulhai, University of Toronto Clarence Woudsma, University of Waterloo Funding Agencies – Infrastructure Canada – IntelliCan Transportation Systems METRANS National Urban Freight Conference 2009 – Oct 21-23, 2009

2. 2 Presentation Overview Research Goal and Objectives Rationale for Exclusive Truck Facilities Background Modelling Preliminary Results

3. 3 Research Goals Assess the potential for highway lanes that are specifically designated for truck travel Develop tools and methods for assessing benefits and impacts.

4. 4 Scope and Study Boundaries 90 km 30 km 160 km 65 km

5. 5 Rationale for Exclusive Truck Infrastructure Mobility  Over 30,000 trucks/day on some segments of Hwy 401 and QEW  Economic value of goods > $1B/day Safety  Trucks involved in 9% of all vehicle collisions  Collisions involving trucks result in 20% of all fatalities Productivity (Longer combination vehicles) Infrastructure Cost (Pavement damage) Revenue Generation (Tolled truck lanes)

6. 6 Background Existing Truck Facilities  New Jersey Turnpike dual-dual section  Los Angeles I-5 Truck Bypass Lanes  South Boston Bypass Road  Clarence Henry Truckway, New Orleans

7. 7 Los Angeles I-5 Truck Bypass Lanes Barrier separated, limited access Interchange bypass

8. 8 Background Corridors Recently Studied  Trans Texas Corridor  Metro Atlanta  SR 60 in Southern California  I-4 Crosstown Connector – Tampa  I-710 Truckway LA  I-81 in VA  Dallas

9. 9 Scoring of Toronto Area Freeways for Potential Truck Infrastructure CriterionWeightThresholdsScore Truck Volume30% 0 – 632 (0 – 75 th percentile) 632 – 752 (75 – 90 th percentile) 752 – 855 (90 – 95 th percentile) 855 – 1,067 (95 – 98 th percentile) 1,067 – 1,170 (> 98 th percentile) 1358913589 Percent Trucks 20% 0 – 7% (0 – 75 th percentile) 7% – 9% (75 – 90 th percentile) 9% – 11% (90 – 95 th percentile) 11% – 14% (95 – 99 th percentile) 14% – 22% (> 99 th percentile) 1578915789 Level of Service 10% A + B C D E F 1235912359 Truck Terminal Accessibility Index 9% 47 – 216 (0 – 75 th percentile) 216 – 243 (75 – 90 th percentile) 243 – 255 (90 – 95 th percentile) 255 – 261 (95 – 99 th percentile) 261 – 287 (> 99 th percentile) 1358913589 Accident Rate5% 0.0 – 1.0 (0 – 75 th percentile) 1.0 – 1.3 (75 – 90 th percentile) 1.3 – 1.8 (90 – 95 th percentile) 1.8 – 2.2 (95 – 99 th percentile) 2.2 – 3.8 (> 99 th percentile) 1358913589 Intermodal Terminal Minimum Distance 9% > 9 miles 7 – 9 miles 5 – 7 miles < 5 miles 05790579 Airport Minimum Distance to Pearson 9% > 9 miles 7 – 9 miles 5 – 7 miles < 5 miles 05790579 Ports Minimum Distance to a Seaport 8% > 9 miles 7 – 9 miles 5 – 7 miles < 5 miles 05790579

10. 10 Scen A–Convert 1 lane to exclusive truck lane on Hwy 401

11. 11 Truck Lanes 2 ft “soft” buffer

12. 12 Scen B – Exclusive Truck Lanes in Hydro Corridor

13. 13 BRT / LRT Truck Lanes Bikeway Hydro Towers

14. 14 Modelling Methods Demand Modelling in EMME2 (trucks and cars) Microscopic Freeway Simulation in Paramics Accessibility Analysis (GIS) Demand and traffic data Travel times Ramp-to-ramp matrices Operational performance and safety analysis Development pressure/ land use benefits and disbenefits Regional OD matrices

15. 15 Demand data Transportation Tomorrow Survey MTO Roadside Commercial Vehicle Survey Region of Peel commercial travel survey Cordon counts Loop detector, intersection, Hwy 407 ETR data Employment data by industry Land use data Toronto Regional EMME2 network Freeway EMME2 network

16. 16 7) GTA Truck Trips 2) GTA Passenger Trips 1) GTA Passenger vehicle OD matrix (expansion factors) Transportation Tomorrow Survey 5) GTA Inter-city truck OD matrices (expansion factors) MTO Commercial Vehicle Survey 6) GTA Intra-urban truck OD matrices (Gravity model) Region of Peel Commercial Travel Survey Land Use Data Employment Data by Industry GTA EMME2 Road Network Multiclass user equilibrium assignment

17. 17 GTA EMME2 Road Network

18. 18 7) GTA Truck Trips 2) GTA Passenger Trips 1) GTA Passenger vehicle OD matrix (expansion factors) Transportation Tomorrow Survey 5) GTA Inter-city truck OD matrices (expansion factors) MTO Commercial Vehicle Survey Model screenline volumes match cordon counts ? Cordon Counts 8) Adjust model parameters No 6) GTA Intra-urban truck OD matrices (Gravity model) Region of Peel Commercial Travel Survey Land Use Data Employment Data by Industry GTA EMME2 Road Network Multiclass user equilibrium assignment

19. 19 Calibration – cordon count boundaries

20. 20 7) GTA Truck Trips Freeway EMME2 Road Network 4) Freeway Passenger Trip Assignment 10) Freeway Truck Trip Assignment 2) GTA Passenger Trips 1) GTA Passenger vehicle OD matrix (expansion factors) Transportation Tomorrow Survey 5) GTA Inter-city truck OD matrices (expansion factors) MTO Commercial Vehicle Survey Model screenline volumes match cordon counts ? Cordon Counts 8) Adjust model parameters No 3) Initial Ramp-to- Ramp Passenger Vehicle OD Matrix 9) Initial Ramp-to- Ramp Truck OD Matrix Yes 6) GTA Intra-urban truck OD matrices (Gravity model) Region of Peel Commercial Travel Survey Land Use Data Employment Data by Industry GTA EMME2 Road Network Multiclass user equilibrium assignment

21. 21 Freeway EMME2 road network

22. 22 7) GTA Truck Trips Freeway EMME2 Road Network 4) Freeway Passenger Trip Assignment 10) Freeway Truck Trip Assignment 2) GTA Passenger Trips 1) GTA Passenger vehicle OD matrix (expansion factors) Transportation Tomorrow Survey 5) GTA Inter-city truck OD matrices (expansion factors) MTO Commercial Vehicle Survey Model screenline volumes match cordon counts ? Cordon Counts 8) Adjust model parameters No 3) Initial Ramp-to- Ramp Passenger Vehicle OD Matrix 9) Initial Ramp-to- Ramp Truck OD Matrix Yes No Model link volumes match road counts ? Freeway Classification Counts 11) OD Matrix Update 6) GTA Intra-urban truck OD matrices (Gravity model) Region of Peel Commercial Travel Survey Land Use Data Employment Data by Industry GTA EMME2 Road Network Multiclass user equilibrium assignment

23. 23 Counts used to calibrate ramp to ramp matrices Loop Detector Counts Cordon counts Ramp counts

24. 24 7) GTA Truck Trips Freeway EMME2 Road Network 4) Freeway Passenger Trip Assignment 10) Freeway Truck Trip Assignment 2) GTA Passenger Trips 1) GTA Passenger vehicle OD matrix (expansion factors) Transportation Tomorrow Survey 5) GTA Inter-city truck OD matrices (expansion factors) MTO Commercial Vehicle Survey Model screenline volumes match cordon counts ? Cordon Counts 8) Adjust model parameters No 3) Initial Ramp-to- Ramp Passenger Vehicle OD Matrix 9) Initial Ramp-to- Ramp Truck OD Matrix Yes No Model link volumes match road counts ? Freeway Classification Counts 11) OD Matrix Update 6) GTA Intra-urban truck OD matrices (Gravity model) Region of Peel Commercial Travel Survey Land Use Data Employment Data by Industry Final Ramp-to-Ramp Truck OD Matrix Yes Final Ramp-to-Ramp Passenger Vehicle OD Matrix Inputs to Microscopic Traffic Simulation GTA EMME2 Road Network Multiclass user equilibrium assignment

25. Microsimulation comparison to freeway counts 25

26. 26 Some Model Results Base case  Values of time: $60/hr light trucks;  $120/hr for medium and heavy trucks A) Convert one lane to exclusive truck lane on Hwy 401  <5 min (7.5%) additional delay for cars (along Hwy 401)  13-21 min (22-33%) savings for trucks (along Hwy 401)  150-750 vph in the truck only lane B) Exclusive Truck Lanes in Hydro Corridor parallel to Hwy 401  1 min (1.5%) savings for cars (along Hwy 401)  3-10 min (5-15%) savings for trucks (along Hwy 401)  17-26 min (20-30%) savings for trucks (along Hydro corridor)  100-800 vph in the exclusive truck lanes

27. 27 Microscopic Traffic Simulation Refining travel time/speed estimates  Car following, lane changing, queuing  Detailed freeway routing Conflict Analysis  Rear-end conflicts  Lane-changing conflicts  Merging conflicts

28. Microscopic Traffic Simulation Captured bottlenecks along freeways 28

29. 29 End!