Peer-to-peer (P2P) ridesharing is a recently emerging travel alternative that can help accommodate the growth in urban travel demand and at the same time alleviate problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, but its true benefits are realized when the demand shifts from single-occupancy vehicles. This study investigated the potential of shifting demand from private autos to transit by providing a general modeling framework that found routes for private vehicle users that were a combination of P2P ridesharing and transit. The Los Angeles Metro Red Line in California was considered for a case study because it has recently shown declining ridership trends. For successful implementation of a ridesharing system, strategically selecting locations for individuals to get on and off the rideshare vehicles is crucial, along with an appropriate pricing structure for the rides. The study conducted a parametric analysis of the application of real-time P2P ridesharing to feed the Los Angeles Metro Red Line with simulated demand. A mobile application with an innovative ride-matching algorithm was developed as a decision support tool that suggested transit-rideshare and rideshare routes.

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride-and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system.

Understanding the spatial variation of taxi ridership is of critical importance to many government agencies and taxi companies because taxis’ location dependency on spatial pattern of passenger demand results in spatially unbalanced taxi demand and supply. This study presents an analysis of the spatial distribution of taxi ridership by using large-scale GPS taxi trip data collected from Seoul, South Korea. To capture the spatial variations better in taxi ridership, GPS entities were disaggregated into units of a uniform size with a grid cell decomposition method. A geographically weighted spatial regression was applied to model spatial correlations of factors associated with transit and urban density to taxi ridership. Results from the proposed method demonstrated a higher relationship between taxi and subway ridership in the regions where lower accessibility to subway stations existed. In these regions, taxis were found to perform as a complementary mode to subway. In residential and commercial districts, this analysis showed that population and employment were highly related to taxi ridership. In contrast, in central business districts it was the building area (floor space), rather than population and employment, that was highly related to taxi ridership.