Advanced Solutions to Capture Mobility Data

Principal Investigator:

Yingling Fan, Assistant Professor, Humphrey School of Public Affairs

Co-Investigator

Project Summary:

Existing smartphone-based travel tracking tools typically maintain a narrow focus by using the smartphone's automatic sensing functionality to detect travel mode and collect position and route data. Such tools generally require a lengthy data collection effort to discover user-specific patterns and preferences and are simply incapable of capturing important contextual information such as user motivations, experiences, and travel companionship. This project will develop SmartTrAC, an Android-based smartphone application that collects highly-detailed, multimodal, and multidimensional travel decision data in order to better understand travel choices and foster transportation advancement. This is possible because of an unprecedented innovation that combines smartphone-based sensing, surveying, and context-aware data-mining technologies with advanced statistical and machine learning techniques to automatically detect, identify, and summarize attributes of daily activity and travel episodes, thereby allowing users to provide additional trip information with minimum disruption to their daily routine. By combining the smartphone's computing and communications capabilities, SmartTrAC will yield travel data of a breadth and depth unavailable from either smartphone sensory data or traditional travel diary methods alone, providing a simple, efficient, low-cost approach to collecting detailed, multimodal, and multidimensional travel data. Subjects will wear their own smartphones equipped with the SmartTrAC app for seven-day tracking periods, which will provide data to the research team regarding data accuracy, user compliance, and hardware performance. Based on previous versions of this application, the research team believes wide-scale deployment is feasible.

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