Real-Time Taxi-Passenger Matching Using a Differential Evolutionary Fuzzy Controller

Real-time taxi-passenger matching plays a critical role in modern taxi dispatch systems. Currently, the greedy strategy is widely adopted, which limits the quality of the service (QoS) and the profit of the entire system. There are two crucial tasks in this system: 1) the pairwise prioritization and 2) the matching of taxi-passenger pairs. In this paper, we develop a two-stage taxi-passenger matching system to deal with these two tasks. In the first stage, we design a fuzzy controller to assign a priority score to each taxi-passenger pair in real time. To ensure its performance on providing good QoS and profit, the fuzzy controller is optimized by an offline differential evolution algorithm. New individual representation is designed to optimize the membership functions and fuzzy rule base simultaneously. To accelerate the optimization process, the algorithm is implemented in a parallel way. Then, in the second stage, considering the priority scores as weights in the bipartite graph of taxi and passenger sets, we further apply a polynomial Kuhn-Munkres algorithm to find the maximum weight perfect matching in the bipartite graph. Simulated results validate the effectiveness of the proposed algorithm, which is able to enhance the QoS provided by the taxi system and improve the profit gained by the taxi service company.

Automated summary

This paper presents a two-stage taxi-passenger matching system that optimizes the quality and profit of taxi-passenger matching by utilizing a fuzzy controller and a polynomial Kuhn-Munkres algorithm.