presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. Metropolitan Council Travel Behavior Inventory Study Overview TRB Applications Conference May Anurag Komanduri

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

TRAVEL BEHAVIOR INVENTORY 3

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” 4

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 5

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 6

TEAM MEMBERS 7

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, Sashank Musti, Monique Urban, Jason Lemp, Ramesh Thammiraju Partners »Laurie Wargelin, Jason Minser (Abt SRBI) »Evalynn Williams, Parani Palaniappan, Martin Wiggins (Dikita) »Angie Christo, Pat Coleman, Srikanth Neelisetty (AECOM) »Peter Stopher, Kevin Tierney, John Hourdos, NexPro

PHASE I MODELING FRAMEWORK 9

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 10

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 11

PHASE II DATA COLLECTION 12

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 13

Data Collection Approach 14 EffortSurvey Complete MediumInnovation Household Activity Survey 14,000+ HHsWeb Mail-back Telephone GPS Effect of incentives on participation Transit On-board Survey 16,000 ridersHand-outs Counts Combine 2005 and 2010 data Special Generator Surveys 330 MoA 550 Airport Personal interviewTablet-based surveys External O-D Survey 5,000 surveysCounts LP capture Mail-back Response Rate > 20% Traffic SpeedsYear-long dataTomTom data purchase TransCAD routines for instant analytics

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 15

PHASE III DATA ANALYSIS & MODELING 16

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

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 18

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

PHASE INFINITY CONTINUOUS LEARNING 20

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

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

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