Peer-to-peer ridesharing is a recently emerging travel alternative that can help accomodate 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 a case study, since it has recently shown declining ridership. A mobile application with an innovative ride-matching algorithm is developed as a decision support tool that suggests transit-ridership and rideshare routes. The app also facilitates peer-to-peer communication of users via smart phones. For successful ride-sharing, strategically selecting locations for individuals to get on/off rideshare vehicles 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 are is conducted.
Promoting Peer-to-Peer Ridesharing Services as Transit System Feeders
2016
40 pages
Report
No indication
English
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