Ride-Sharing: a multi source-destination path planning approach

Authors:
Jamal Yousaf;Juanzi Li;Lu Chen;Jie Tang;Xiaowen Dai;John Du
Affiliations:
Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China;China Science Lab, General Motors, Shanghai, China;Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China;China Science Lab, General Motors, Shanghai, China;China Science Lab, General Motors, Shanghai, China
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

Ride-sharing is considered as one of the promising solutions for reducing fuel consumption of fuel and reducing the congestion in urban cities, hence reducing the environmental pollution. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired characteristics of ride-sharing systems. Ride-sharing systems can be popular among people if we can provide more flexible and adaptive solution according to preferences of the participants and solve the social challenges. In this paper, we focus on encouraging people to use a ride-sharing system by satisfying their demands in terms of safety, privacy, convenience and also provide enough incentives for drivers and riders. We formalized the ride sharing problem as a multi source-destination path planning problem. An objective function is developed which models different conflicting objectives in a unified framework. We provide the flexibility to each driver that he can generate the sub-optimal paths according to his own requirements by suitably adjusting the weights. These sub-optimal paths are generated in an order of priority (optimality). The simulation results have shown that the system has the potential to compute multiple optimal paths.