Crowdsourcing the Collection of Transportation Behavior Data

The goal of our proposed research project is to develop a mobile application for crowdsourcing the collection of travel behavior data on public transit. Understanding the behavior of individual commuters is essential for maintaining sustainable transportation systems. However, travel behavior is complex and composed of numerous elements of a commuter's experience beyond the location and time of when one enters or exists vehicles. However, such individualized data can be prohibitively expensive and time-consuming to collect and understand, especially for smaller agencies in which resources are limited. Utilizing our existing strengths in research, technology, and education at the University of Oregon,…

The goal of our proposed research project is to develop a mobile application for crowdsourcing the collection of travel behavior data on public transit. Understanding the behavior of individual commuters is essential for maintaining sustainable transportation systems. However, travel behavior is complex and composed of numerous elements of a commuter’s experience beyond the location and time of when one enters or exists vehicles. However, such individualized data can be prohibitively expensive and time-consuming to collect and understand, especially for smaller agencies in which resources are limited.
Utilizing our existing strengths in research, technology, and education at the University of Oregon, our research team proposes to develop a mobile crowdsourcing data collection application to be used by riders who will be prompted to provide transportation data when entering, riding, and exiting public transportation vehicles. We will design the application in coordination with a collaborator transportation agency, evaluate the potential of multiple user-incentive mechanisms, and provide data analytics capabilities in order that agencies can easily utilize the crowdsourced data for decision-making. Our proposed application is infrastructure-free, as there is in no need to purchase hardware on buses or at transit stops, and is therefore easily replicable and can be implemented with minimal barriers to entry for the nation’s transit operators. Furthermore, our proposed project provides multiple education opportunities by way of incorporating undergraduate students in design and testing, and utilizing graduate student involvement throughout all stages of the project. Our mobile application and associated data analytics will be made publically available, and will be accompanied with reference materials directed to researchers, practitioners and educators.