1. DVRPC TMIP Peer Review TIM 2 Model Oct. 29 th, 2014

2. Travel Models - Overview TIM1.0 First VISUM model, completed in 2009 TIM 2.0 “Best-in-class” 4-step model Networks carry forward TIM 2.1 & TIM 2.2 Minor bug fixes and improvements Tim 3.0 Fully disaggregate microsimulated activity based

3. Travel Models – TIM 2.0  Classical 4-step model  4 times-of-day  10 trip purpose x income types  Limited bike and walk trip model  Transit by access mode – walk vs. drive  Large network – 26 counties  Open source networks – OSM and GTF  Iterative feedback for system convergence  12-hours to run, 90 GB disk space, 48+GB RAM

4. Model Enhancements Larger area More time periods More detailed highway network More Traffic Analysis Zones Better representation of transit service and fares More socioeconomic variables More trip purposes Better treatment of non-motorized travel Improved model operations

5. Extension of the Model Area  Simplified representation of adjacent Counties  Expected benefits:  Full coverage of DVRPC’s home-work travel shed  Easier start-up for studies across MPO boundaries

6. The OpenStreetMap  www.osm.org  Started in 2004  Organization: OSM foundation non-profit, based in the U.K.  Volunteers  They generate the map Upload data from their private GPS devices Edit directly on www.osm.org  Data distribution  Free of charge  Can be used for any commercial or non-commercial purpose  Data content  Routable street network plus other geography  U.S. data derived from an import of the 2005 TIGER file

7. GTFS Overview  TriMet/Google developed specification  Widely adopted standard for public transit  Series of text files with comma-delimited values  (GTFS = General Transit Feed Specification)

8. Open Data Mash-up for Modeling  Data integration Data objects of different origin are merged New relationships are created from OSM Stop Point Number Line Name Service Pattern Line Name Route Name Direction Scheduled Run Line Name Route Name Direction Index Travel Demand Data Stop Area Number from GTFS Node Number Link From Node To Node 2 1 or more 0 or more Exactly 1 Legend Connector Zone Number Node Number Direction Zone Zone Number

9. The TIM 2.0 Network in Numbers Number of network objects TIM1.0TIM2.0 Street segments 50,000580,000 Transit stops (stop points) 5,00018,000 Transit service patterns 2,0006,000 TAZ (traffic analysis zones) 2,0003,400

11. More Detailed Highway Network  TIM1.0 TIM2.0

12. Integrated Street & Transit Network

13. Highway Assignment Example

14. Demand Model Conventional 4-step model Uses “Hotstart” approach – generic trip table and PnR lot choice pre- loaded for quick convergence 12 hours run time 5/5/5/3 iterations for AM/MD/PM/NT Trip purpose segregation by income TD & MC done using nested logit model in VISUM Trip Generation Trip Distribution Mode Choice Highway Assignment TIM 2.0 Flow

15. Trip Purposes Home based work Low income High income Home based shop Low income High income Home based school Home based university Home based other Low income High income Non- home based work Non home based other

16. Trip Generation TOD TG Balancing Directional Factoring to TD Trip Gen Approach: Ps As Os Ds Ps As Daily trip generation using rates and demographic variables Motorized / non-motorized mode split using logit models Time of day factoring Directional factoring to Os and Ds

17. Socioeconomic Variables Households by Size Number of workers Number of autos Income category School Enrollment K-12 University NAICS Employment Professional services Eds and Meds Arts/Rec/Food services Other services Land Use Variables Parking cost Retail density Land use mix Concentration of low income households

18. Trip Distribution & Mode Choice Trip distribution in O-D format (e.g. Home to Work handled separately from Work to Home) using gravity model Transit trips nested by mode of approach All trips HighwayTransit Transit Walk Transit Auto

19. TIM 2.0 Park and Ride Modeling  Basic Method:  Step 0 – Prepare TIM 2.0 zone system (6 virtual P&R zones)  Step 1 – Obtain highway (i  k) and transit walk-access (k  j) skims  Step 2 – Matrix Convolution – determine i  j skim matrix  Step 2.1 Determine Optimal Lot (k) for each i  j pair  Step 2.2 Compose i  j skim from highway portion (i  k) and transit portion (k  j)  Step 3 – Determine Auto access trip table via nested logit model  Step 4 – Split and assign joint auto-transit trip  Step 4.1 Split i  j joint trip into highway and transit portions  Step 4.2 Add i  k portion to total auto trip table and assign  Step 4.3 Add k  j portion to transit-walk trip table and assign Terminology i – auto trip end k – P&R zone j – transit trip end

20. Model execution Masterbody script handles overall model execution Mixture of “canned” VISUM functions and python scripts ~90 GB free disk space needed At least 16 GB RAM for sequential execution At least 64 GB RAM for parallel execution Visum will use as much computational power as you can provide (current machines have 12 cores)

21. Overlord

22. Calendar Scheduler

23. Master Script (Python) Daily Trip Gen AM Model Midday Model PM Model Initial Main Body Imp. Averaging Trip Dist. Mode Choice Hwy. Assignment End Implementation of TIM 2.0 in VISUM – Model Architecture Night Model

24. Reporting Tools

25. Validation Reports