|Principal Investigator||Didier Valdés, Ph.D.|
|Final Report (DOI)||Available Soon|
|Policy Brief||Available Soon|
|RIP||View RIP entry|
This project will use the open-source platform OneBusAway (OBA) developed at USF to explore how travelers choose between public transit, transportation network companies (TNC), and other methods (e.g., micro-mobility) and how to influence this behavior to increase transit ridership. The project will focus on first and last-mile connections, filling gaps in the transit network and vulnerable populations such as older adults and people with disabilities. The project will also explore how alternative methods can assist transit during emergencies by studying how mobility service providers can better support transit operations (e.g., route replacement, optimization, seamless mobility platforms for routing, booking, and payment; and how best to “message” travelers in real-time (via texts, emails, etc.) to nudge them toward making congestion reducing travel choices like taking public transportation. OneBusAway currently provides real-time transit information to users on a full range of devices and communication platforms, such as mobile apps, and serves more than 400,000 individuals across ten cities in the United States. Phase I implementedthis open-source platform in Mayagüez, Puerto Rico. The UPRM NICR team, with the support of USF developers of OBA, coordinated the activities to implement OneBusAway on two bus services operating in Mayagüez. Collected georeference data corresponding to these transit systems influence area will be recorded in a geographic database. Focus groups, ridership studies, and a travel survey will provide further data from the activity system. Behavioral data collected using a new survey tool developed and embedded in the OBA platform to solicit information on users’ travel choices and their motivations will also be integrated into the georeferenced database. Statistical and econometric methods will be used to analyze focus groups and to determine what influences travel preferences. Spatial econometric models incorporating all the collected data will be estimated and used to test various strategies to increase ridership.