Mobile Apps and Transportation: Exploring Data Metric Potential and User Response to Multi-Modal Traveler Information

Mobile Apps and Transportation: Exploring Data Metric Potential and User Response to Multi-Modal Traveler Information
PI: Susan Shaheen, UC Berkeley
$99,782

Abstract: The proliferation of innovative mobility options within American cities in recent years has greatly enhanced transportation alternatives for the public, but it has simultaneously presented a challenge for transportation planners. Mobility resulting from increased multi-modalism is best measured through person miles traveled (PMT) as opposed to vehicle miles traveled (VMT). However, PMT is measured through comprehensive but infrequent surveys. As travel becomes more multi-modal, there are challenges associated with tracking that behavior. However, mobility “apps,” such as multi-modal trip aggregators, can play a role in better understanding this behavior. First, many of these apps have a customer base engaging in multi-modal behavior. Second, these apps collect real-time information about travel/trip options as part of their operation. Together, these two features provide an opportunity to study users to better understand multi-modaldecision making as well as to discover how data collected by mobility apps could be used to inform better PMT measures for transportation planners. After conducting a literature review, the research team will develop a matrix of leading and emerging smartphone apps to provide an understanding of the range and potential of these tools to support transportation demand management and planning. Further, researchers will conduct expert interviews with mobility app designers to develop designs for data protocols that could be used to generate PMT measurements. Through a partnership with RideScout, a leading mobility app, this study will develop a deeper understanding of multi-modal decision making and impacts through a user survey. This study will synthesize these results into a report that advances understanding of multi-modal travel facilitated through 2 apps, as well as to discuss potential pathways for using app data to more dynamically measure PMT.

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