Metropolitan Council Travel Behavior Inventory

1. Metropolitan Council Travel Behavior Inventory Study Overview TRB Applications Conference May 8 2013 Anurag Komanduri

2. Presentation Outline • What I did for the last three summers • Travel Behavior Inventory - Overview • Data Collection • Modeling Framework • Lessons Learned & Future Vision

3. Travel Behavior Inventory

4. TBI Goals • Snapshot of personal travel in Minneapolis-St. Paul • Collect and provide quality data • Stand-alone data products • Regional initiatives + research • Travel demand modeling • Build a fine-grained policy-sensitive model using data • State of the practice activity-based model • “Create a lasting legacy for the region”

5. TBI Approach • Perform study in phases • Phase I – Survey design • Phase II – Data collection and processing • Phase III – Model development and calibration • Set goal + allocate resources • Be flexible – needs change • Reset and reload • Regular updates • Doses of (dis)agreement better than ONE shouting match • “Keep it simple – do it well” • Innovate incrementally

6. TBI Challenges • Balance innovation with pragmatism • Big team • Manage roles…budgets..schedules.. • Project management role - important • Data management – “where do pieces fit in” • Multi-year schedule • 2010 – Ongoing • Stay focused…pay attention

7. TEAM MEMBERS

8. Staff on Project • Metropolitan Council + PMT • Jonathan Ehrlich, Mark Filipi (Met Council) • David Levinson (U-Minn), Jim Henricksen (MnDOT) • CS Staff • Kimon Proussaloglou (Project Manager) • Anurag Komanduri (Deputy PM) • Thomas Rossi , David Kurth (Senior Advisors) • Brent Selby, Daniel Tempesta, Cemal Ayvalik, SashankMusti, Monique Urban, Jason Lemp, RameshThammiraju • Partners • Laurie Wargelin, Jason Minser (AbtSRBI) • Evalynn Williams, ParaniPalaniappan, Martin Wiggins (Dikita) • Angie Christo, Pat Coleman, SrikanthNeelisetty (AECOM) • Peter Stopher, Kevin Tierney, John Hourdos, NexPro

9. Phase IModeling Framework

10. Modeling Framework - Approach • Evolving process • Conceived as a hybrid trip + tour model • Upgraded to an activity-based model • Impact on data analysis • Tour structures for “all” trips • Greater emphasis on household activity survey • Budget + schedule • Seek efficiencies • Revise scope (always fun!) • Model estimation + validation • Intricate modeling framework • “Nuanced” validation

11. Modeling Framework – Key Features • Model design plan – during data collection • Committee buy-off • Custom activity-based model • Assess “forecastable” data • Locally relevant models (toll transponder ownership) • Utilize efficiencies, wherever possible • PopGen developed by ASU • Benchmark against HGAC models • Modeling sequence • Estimation order – application order

12. PHASE IIDATA Collection

13. Data Collection Goals • Collect travel behavior data • Household travel surveys – year long effort, seasonality • On-board surveys • Special generators – Mall of America, Airport • External surveys • Update supply-side information • Highway counts and speed profiles • Transit ridership counts • Park-and-ride utilization • Parking lots – space and costs • Networks – highway, transit, bike-ped • Variety of collection methodologies • Horses for courses

14. Data Collection Approach

15. Data Collection Challenges • Household survey • “Hard to reach” population • Lower participation from “working households” • GPS assessment • On-board survey • Limited budget • Expand data to match “true” ridership patterns • Special generator survey • Poor response rates • External O-D survey • Time consuming – license plate capture, mail-back survey

16. PHASE IIIData Analysis & MODELING

17. Data Analysis – Approach • Data preparation – multiple steps • Data transfer protocols • Delivery dates… more delivery dates… yet more… • Geocoding • QA/QC routines • Expansion • Assign gate-keepers for “surveys” • Version control • Survey database experts • Data utilization approach • Evolving process – model design plan

18. Dataset Utilization • Household activity survey • Estimation dataset • Primary validation dataset • Transit on-board survey • No tours - not used in estimation • CRITICAL validation component • Special Generator survey – validation • O-D survey – external model • Airport survey – visitor model • TomTom speeds + Traffic counts • Free flow speeds • BPR curve sensitivity testing

19. I-94: from TH 61 to I-35E AM Shoulder AM Peak Mid-day PM Early PM Peak Evening late Overnight

20. PHASE INFINITYCONTINUOUS Learning

21. Things we picked up along the way… • Myth 1 – TRAVEL DATA CAN BE MADE PERFECT • Travel surveys are complex…respondents “trip up” • “Cleaning” is great, but impact tails off • Myth 2 – UNOBTRUSIVE DATA ARE PERFECT • Still dependent on human behavior • Cracking the GPS paradigm – close, but not 100% • Myth 3 - LOCAL EXPERTISE IS KEY • Team from 9 states (including MN) • “Open communication” channels key • Myth 4 – MIDWESTERNERS ARE POLITE • Not a myth • Fabulous response rates • O-D mail-back had response rate of about 20 percent

22. Things we picked up along the way… • Collecting large data repositories is fabulous • All data from the same timeframe • Great for modeling • Requires strong team working together • Travel behavior is changing • Fewer overall trips • Increased bike usage • Travel data are becoming ubiquitous – overwhelming! • Highway - Speed data, counts • Transit - Farebox, AVL and APC data • Personal travel – cell phone data, GPS logs, smartcard usage, toll transponder transactions • Freight (not used) – GPS logs