Real-Time City-Scale Taxi Ridesharing

• Published in: IEEE transactions on knowledge and data engineering Vol. 27; no. 7; pp. 1782 - 1795
• Main Authors: Shuo Ma, Yu Zheng, Wolfson, Ouri
• Format: Journal Article
• Language: English
• Published: IEEE 01-07-2015
• Subjects: GPS trajectory; Indexes; intelliegent transportation systems; Real-time systems; ridesharing; Roads; Schedules; Servers; Smart phones; Spatial databases and GIS; taxi-sharing; urban computing; Vehicles; urban computing; GPS trajectory; intelliegent transportation systems; Spatial databases and GIS; taxi-sharing; ridesharing
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2014.2334313

We proposed and developed a taxi-sharing system that accepts taxi passengers' real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ride sharing and get compensated if their travel time is lengthened due to ride sharing; taxi drivers will make money for all the detour distance due to ride sharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/~sma/ridesharing) in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system demonstrated its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.