A Critical Review of Subway Train Timetabling and Rescheduling Problems

Train timetabling plays a major role in railway planning processes, serving as a link between service providers and commuters to ensure reliable service delivery. However, mathematical optimization application to expansive subway systems is uncertain due to challenges in coordinating multiple lines, the necessity for integration with passenger demand, and multi-modal coordination. This study comprehensively reviews three main sub-stages of timetabling research areas: nominal, robust, and rescheduling problems. We synthesized the relevant studies considering the mathematical modeling of designed strategies, the incorporated stakeholders’ views, the characteristics of solution methods, and the extent of their practical use. Recent efforts to enhance subway system resiliency have had a limited focus on applying artificial intelligence methodologies to subway train timetabling, particularly emphasizing rescheduling problems. In the future, the research community is expected to broaden the application of artificial intelligence to encompass various aspects of subway train timetabling, incorporating four suggested considerations. These include coordination between multiple traffic modes, risk control, deep learning and mathematical optimization, and carbon peaking and neutrality goals to achieve standardized net-zero carbon emission objectives. These considerations represent a strategic pathway towards the next generation of subway train control systems. To conclude, the study aims to introduce theoretical aspects and research gaps in subway timetabling for the dedicated subway network and to provide future research directions.