A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

Abstract

Efficient route optimization is vital for ensuring both safety and punctuality in railway operations. The challenge is particularly pronounced in heterogeneous multi-gauge railway networks, where varying train speeds, stopping patterns and infrastructure compatibility constraints increase coordination complexity. In single-track systems, these challenges are further intensified because trains share the same track and often require frequent track switching. Stochastic disruptions, such as blocked tracks, blocked trains, engine failures and speed slowdowns, add further uncertainty and disrupt the timetable. However, existing studies predominantly focus on high-level timetabling and often omit operational details such as track-switching coordination. Therefore, this study proposes a framework based on temporal planning for route optimization and disruption management in heterogeneous railway systems. The proposed framework formulates railway operations as a temporal planning problem, explicitly modeling gauge compatibility constraints and diverse disruption scenarios. It generates conflict-free, timestamped operational plans that provide coordinated schedules together with executable action sequences. To evaluate the framework, we have developed a benchmark of 200 instances with up to 1,000 track points and 120 trains, and evaluated the framework on this benchmark using two state-of-the-art temporal planners and a plan validator. The experiments show that the proposed model can generate valid temporal plans across a wide range of heterogeneous railway scenarios with multi-gauge constraints and disruption-recovery requirements. These findings demonstrate the feasibility of applying temporal planning techniques to disruption-aware railway routing and scheduling problems.