Dynamic operations of an integrated mobility service system of fixed-route transits and flexible electric buses. (May 2023)

• Record Type: Journal Article
• Authors: Tang, Xindi; Yang, Jie; Lin, Xi; He, Fang; Si, Jinhua
• Is Part Of: Transportation research. Volume 173(2023)
• Publication Date: 2023-05
• Subjects: Integrated mobility service system -- Dynamic operations -- Learning-and-optimization -- Flexible bus -- Electric vehicle; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2023.103081
• Languages: English
• ISSNs: 1366-5545

Abstract

Abstract: As an emerging mobility service with a wide range of benefits, flexible buses balance monetary costs, travel time, and comfort, and have evolved into a powerful supplement to traditional travel modes. This study investigates the dynamic operations of an integrated mobility service system of flexible electric buses and fixed-route transits, such as traditional buses and metros. The proposed integrated framework owns the potential of making the maximum utilization of the flexibility of mobility-on-demand transports and the capacity of fixed-route transits, but it is very challenging to efficiently operate. We establish mathematical models to dynamically optimize the detailed flexible bus routing and timetabling plans, and assign travel routes for passengers while considering the transfer behavior from/to the conventional fixed-route transits in a network. To resolve the curse of dimensionality and facilitate the solution procedure, we embed a learning procedure into an optimization model and further decompose the subproblem in each period into two layers. Numerical studies verify the superior performance of the proposed learning-and-optimization framework in both stochastic dynamic and deterministic settings. Highlights: Online operations framework for an integrated system composed of fixed-route transits and flexible electric buses. Learning-and-optimization framework to consider system-wide coordination and long-term impact. Bi-level decomposition of the subproblem in each period to facilitate the solution procedure. Numerical studies to verify the superior performance in both stochastic dynamic and deterministic settings. Sensitivity analyses on key factors and managerial insights.