Review on Train Timetabling and Rolling Stock Circulation Planning Problems for Urban Rail Transit Systems

The train timetable and rolling stock circulation plan are crucial aspects of urban rail transit operation planning which serve as the foundation for operating companies to reduce costs, improve efficiency, and enhance quality.This paper systematically reviews and summarizes the latest research advancements in the field of train timetabling and rolling stock planning problems.The review is organized from three perspectives: studied problems, modeling methods, and solution algorithms.In terms of studied problems, existing literature focused on train timetabling optimization, the joint optimization of the train timetable and rolling stock circulation plan, and train timetabling optimization with variable train compositions.Regarding modeling methods, conventional integer programming and discrete space-time network approaches have emerged as the two mainstream approaches for modeling train timetabling and rolling stock circulation planning problems.In terms of solution algorithms, heuristic methods are widely applied due to their simplicity, flexibility, and efficiency in solving train timetabling and rolling stock circulation planning problems.Exact solution algorithms, which offer global search and solution quality assessment mechanisms, are currently mainstream.Additionally, reinforcement learning methods have also been preliminary applied to solving the aforementioned problems.Future research needs to focus on the innovation of train operation modes brought about by technological changes in communication, control, and other areas, as well as the new problems derived from them.It is necessary to explore the integration of urban rail transit with other transportation modes, and design high-performance solving algorithms to address the computational challenges of large-scale urban rail transit operation management issues.