presented to

presented byCambridge Systematics, Inc.

Transportation leadership you can trust.

Metropolitan Council Travel Behavior InventoryStudy Overview

TRB Applications Conference

May 8 2013

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

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TBI GoalsSnapshot 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”

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TBI ApproachPerform 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

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TBI ChallengesBalance 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

TEAM MEMBERS

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Staff on ProjectMetropolitan 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 IMODELING FRAMEWORK

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Modeling Framework - ApproachEvolving 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

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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

PHASE IIDATA COLLECTION

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Data Collection GoalsCollect 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

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Data Collection ApproachEffort Survey

CompleteMedium Innovation

Household Activity Survey

14,000+ HHs WebMail-backTelephoneGPS

Effect of incentives on participation

Transit On-board Survey

16,000 riders Hand-outsCounts

Combine 2005 and 2010 data

Special Generator Surveys

330 MoA550 Airport

Personal interview Tablet-based surveys

External O-D Survey

5,000 surveys CountsLP captureMail-back

Response Rate > 20%

Traffic Speeds Year-long data TomTom data purchase

TransCAD routines for instant analytics

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Data Collection ChallengesHousehold 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

PHASE IIIDATA ANALYSIS & MODELING

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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

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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

AM Shoulder

AM Peak

Mid-day

PM Early

PM Peak

Evening late

Overnight

I-94: from TH 61 to I-35E

PHASE INFINITYCONTINUOUS LEARNING

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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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