Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

Abstract

As aging populations increase worldwide, many governments have introduced the concept of paratransit services to assist individuals with limited mobility with transportation. A successful paratransit service must be able to satisfy most requests to the system; this success is typically related to the allocation of vehicles to dispatch stations. A suitable configuration can reduce unnecessary travel time and thus serve more people. This resembles the classic Dial-a-Ride problem, which previous studies have solved using heuristic algorithms. Most of these algorithms, however, incur heavy computational costs and, therefore, cannot be operated online, especially when there are many conditions to consider, many configuration requirements, or many vehicles requested. Therefore, this paper proposes an approach based on the generative adversary network (GAN), which can reduce computation significantly. In online environments, this approach can be implemented in just a few seconds. Furthermore, the amount of computation is not affected by the number of conditions, configuration requirements, or vehicles requested. This approach is based on three important concepts: (1) designing a GAN to solve the target problem; (2) using an improved Voronoi diagram to divide the overall service area to generate the input of the GAN generator; (3) using well-known system simulation software Arena to swiftly generate many conditions for the target problem and their corresponding best solutions to train the GAN. The efficiency of the proposed approach was verified using a case study of paratransit services in Yunlin, Taiwan.