Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

January 20, 2020

The paper “Multi-objective predictive taxi dispatch via network flow optimization“, authored by Beomjun Kim, Jeongho Kim, Subin Huh, Seungil You, and Insoon Yang, has been accepted to IEEE Access. This work was done in collaboration with Kakao Mobility. It is one of the most industry-oriented contributions made by our lab.

Abstract: In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride requests as possible while minimizing the cost of relocating vehicles. To obtain the desired solution, we first convert the multi-objective taxi dispatch problem into a network flow problem, which can be solved using the classical minimum cost maximum flow (MCMF) algorithm. We show that a solution obtained using the MCMF algorithm is integer-valued; thus, it does not require any additional rounding procedure that may introduce undesirable numerical errors. Furthermore, we prove the time-greedy property of the proposed solution, which justifies the use of receding horizon optimization. For computational efficiency, we propose a linear programming method to obtain an optimal solution in near real time. The results of our simulation studies using the real-world data for the metropolitan area of Seoul, South Korea indicate that the performance of the proposed predictive method is almost as good as that of the oracle that foresees the future.

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