A Novel Taxi Dispatch System Integrating a Multi-Customer Strategy and Genetic Network Programming
QingBiao Meng, Shingo Mabu, Lu Yu, and Kotaro Hirasawa
Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
Taxi service usually benefits people by providing comfortable and flexible ride experiences. However, an inherent problem, the insufficient number of taxis at traffic peak, has baffled taxi service ever since it existed. This paper hereby proposes a Multi-Customer Taxi Dispatch System (MCTDS), where taxis are granted a right to take customers with different Origin-Destination (OD) pairs simultaneously, to shorten the total waiting time and traveling time. In addition, to mitigate the damage of detours, MCTDS is built based on Genetic Network Programming, a graph-based evolutionary algorithm that has shown excellent performances previously in some complicated applications. We also modify the structure of GNP to achieve an improvement in performance. In the simulation part, we demonstrate that MCTDS outperforms the conventional GNP and some heuristic taxi dispatch approaches.
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