Efficient Real-time Rail Traffic Optimization: Decomposition of Rerouting, Reordering, and Rescheduling Problem

TL;DR

This paper introduces multi-stage heuristic models for real-time railway traffic management, effectively reducing computation time while handling rerouting, reordering, and rescheduling to address disruptions and conflicts.

Contribution

It extends existing MILP models by incorporating safety overlaps and proposes decomposed heuristics for faster, realistic traffic management solutions.

Findings

Multi-stage heuristics significantly reduce optimization runtime.

Decomposition improves solution realism and efficiency.

Models perform well across various traffic scenarios.

Abstract

The railway timetables are designed in an optimal manner to maximize the capacity usage of the infrastructure concerning different objectives besides avoiding conflicts. The real-time railway traffic management problem occurs when the pre-planned timetable cannot be fulfilled due to various disturbances; therefore, the trains must be rerouted, reordered, and rescheduled. Optimizing the real-time railway traffic management aims to resolve the conflicts minimizing the delay propagation or even the energy consumption. In this paper, the existing mixed-integer linear programming optimization models are extended considering a safety-relevant issue of railway traffic management, the overlaps. However, solving the resulting model can be time-consuming in complex control areas and traffic situations involving many trains. Therefore, we propose different runtime efficient multi-stage heuristic models by decomposing the original problem. The impact of the model decomposition is investigated mathematically and experimentally in different rail networks and various simulated traffic scenarios concerning the objective value and the computational demand of the optimization. Besides providing a more realistic solution for the traffic management problem, the proposed multi-stage models significantly decrease the optimization runtime.