Hierarchical Vehicle Scheduling Research on Tide Bicycle-Sharing Traffic of Autonomous Transportation Systems

Abstract

To facilitate the intelligent and automated provision of mobility services by autonomous transportation systems, bike-sharing can be a supplement to public transport for addressing their point-to-point issue, namely, “last mile” service. However, according to the different nature of land use, the uneven spatio-temporal distribution of travel demand can directly lead to difficult access to bikes with high travel costs for users and operating costs for operators. Based on this, this paper analyzes the user behavior patterns within different areas by using GeoHash encoding and proposes a hierarchical autonomous vehicle scheduling model based on tide bicycle-sharing traffic, namely, HATB. It minimizes operating costs and maximizes user satisfaction to dynamically optimize scheduling routes and required vehicles within each layered zone. As for extracting historical orders of Mobike in Beijing, an example analysis through the genetic algorithm of HATB is conducted to support effective and efficient scheduling. Compared to existing scheduling methods, HATB has faster convergence and lower time complexity, which improves bike turnaround efficiency and practical application ability, thus making it more convenient for the public to travel during peak hours.