Improving the Quality and Cost Effectiveness of Multimodal Travel Behavior Data Collection
 A recording is available for a CUTR Transportation Webcast presented by Sean J. Barbeau, Ph.D., from the Center for Urban Transportation Research. Multimodal transportation such as transit, bike, walk, transportation network companies (TNCs) (e.g., Uber, Lyft), car share, and bike share are vital to supporting livable communities. However, current data collection techniques for multimodal travel behavior, including apps built specifically for travel behavior surveys, have limitations (e.g., significant negative impact on battery life, user acquisition) which prevent a better understanding of significant real-world challenges (e.g., multimodal traveler choices, relationships between travel behavior and health).
This webinar discusses the results of a recently completed research project funded by the National Center for Transit Research, “Improving the Quality and Cost Effectiveness of Multimodal Travel Behavior Data Collection”. In this project, the research team developed and deployed a proof-of-concept system to collect multimodal travel behavior data on an ongoing basis directly from users of a popular open-source mobile app for multi-modal information, OneBusAway (OBA). To overcome battery life challenges, the research team used the Android Activity Transition API, which leverages hardware advancements in modern mobile phones.
This webinar presents the technology used to implement this data collection tool, as well as the results of a pilot deployment to 676 beta testing users. Over 10 weeks, 74 users opted into the study without any incentive and contributed 65,582 trips. Key concerns discussed for data collection when conserving battery life include the timeliness and accuracy of data.
This webcast was hosted by the Center for Urban Transportation Research (CUTR) at the University of South Florida (USF). This project was sponsored by the National Center for Transit Research at USF with funding from USDOT.
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