Design of Flexible Transit with Multitype Stations Considering Spatiotemporal Heterogeneity

Abstract

Flexible transit is an alternative to fulfilling personalized and uncertain demand. This paper describes the design of a flexible transit system with multitype stations and a hybrid time window operating pattern to accommodate the spatiotemporal heterogeneity of passenger demand. After analyzing data from multiple sources, the fixed transit stations can be selected and classified into critical fixed stations that have strong punctuality requirements and uncritical fixed stations that do not have strong punctuality requirements. Additionally, potential request stations are identified using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Catering to the benefits to passengers and the operating agency, an optimization model for vehicle scheduling and routing is developed. Subsequently, a genetic algorithm to solve the proposed model is designed. Finally, a detailed application analysis based on real-world data is carried out to validate the proposed flexible transit system. Compared with the other two flexible transit services, the proposed flexible transit service with multitype stations is more effective in terms of reducing the total cost and average passenger waiting time significantly, while guaranteeing the number of passengers served by the flexible transit system. In addition, results indicate that the flexible transit system is able to adapt to varying demand. The flexible transit service proposed in this paper can be applied to most cities characterized by the spatiotemporal heterogeneity of passenger demand, subsequently promoting the development of diversified bus service in these cities.