Real-time Rail traffic Management in Closed-loop: Assessment of Alternative Train Route Selection

The real-time Railway traffic Management Problem (rtRTMP) is key for optimizing train schedules during perturbations, aiming to minimize delay propagation and enhance the overall reliability of the railway network. However, solving the rtRTMP becomes computationally challenging when trains have many alternative routes. To address this, the Train Route Selection Problem (TRSP) was introduced to reduce the solution space by limiting the number of train alternative routes before solving the rtRTMP. This study assesses the impact of applying the TRSP in a closed-loop framework that integrates railway traffic simulation with a decision making module, which optimizes train schedules and routes in real-time using the state-of-the-art RECIFE-MILP solver. We explore two TRSP application strategies within this framework: tactical and operational. This is the first study to test the TRSP in a realistic deployment setting. We perform computational experiments on the Saint-Lazare station control area in France. We also vary user-defined settings that govern the execution of the closed-loop framework referred to as meta-parameters—such as the call interval, the computational time limit, and the optimization time horizon, to analyze their influence on performance.