Project Summary
Peer-to-peer ridesharing services are a recently emerging travel option that can help accommodate the growth in urban travel demand, and alleviate some of the current problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, while its true benefits are obtained only if the demand shifts from private autos. This project studies the potential of efficient real-time ride-matching algorithms to augment demand for transit by reducing private auto use. The Los Angeles Metro red line is considered for the case study, since it has recently shown declining ridership. A mobile application with an innovative ride-matching algorithm will be developed as a decision support tool that suggests routes that combine ridesharing and transit. The app also facilitates peer-to-peer communications of users via smart phones. For successful ride-sharing, strategically selecting transit stations is crucial, along with the pricing structure for rides. These can be adjusted dynamically based on the feedback from the app-users. A parametric study of the application of real-time ride-matching algorithms using simulated demand in conjunction with the SCAG model for the selected study area is proposed, along with a limited field study of the peer-to-peer use of the apps.
