To support an efficient carpooling service in heavy urban traffic, we propose an intelligent routing scheme based on mining Global Position System trajectories from shared riders. The carpooling system provides many-to-many services with multiple pickup and dropping points. To join a daily carpooling group, the riders must accept a compromised route that is efficient after merging the routes that are preferred by all qualified riders. We developed three frequency-correlated algorithms for route mining, rider selection, and route merging in an urban carpool service. Our approach can cope with the traffic dynamics to yield a suboptimal shared route. Our scheme was successfully tested under heavy Beijing traffic over hundreds of riders. We developed performance metrics to measure the service cost and mileage saved. The ultimate goal is to minimize the riding distances and the transportation costs, and thus alleviate urban traffic jams.
Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories
IEEE Transactions on Intelligent Transportation Systems ; 15 , 5 ; 2286-2296
2014-10-01
1445715 byte
Article (Journal)
Electronic Resource
English
Impact of Traffic Conditions and Carpool Lane Availability on Peer to Peer Ridesharing Demand
ArXiv | 2019
|CARPOOL REQUEST METHOD, CARPOOL REQUEST DEVICE, AND PROGRAM
European Patent Office | 2015
|CARPOOL SUPPORT SYSTEM, CARPOOL SUPPORT METHOD, PROGRAM AND MOVABLE BODY
European Patent Office | 2019
|CARPOOL SUPPORT SYSTEM, CARPOOL SUPPORT METHOD, PROGRAM AND MOVABLE BODY
European Patent Office | 2019
|