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The railway timetables are designed in an optimal manner to maximize thecapacity usage of the infrastructure concerning different objectives besidesavoiding conflicts. The real-time railway traffic management problem occurswhen the pre-planned timetable cannot be fulfilled due to various disturbances;therefore, the trains must be rerouted, reordered, and rescheduled. Optimizingthe real-time railway traffic management aims to resolve the conflictsminimizing the delay propagation or even the energy consumption. In this paper,the existing mixed-integer linear programming optimization models are extendedconsidering a safety-relevant issue of railway traffic management, theoverlaps. However, solving the resulting model can be time-consuming in complexcontrol areas and traffic situations involving many trains. Therefore, wepropose different runtime efficient multi-stage heuristic models by decomposingthe original problem. The impact of the model decomposition is investigatedmathematically and experimentally in different rail networks and varioussimulated traffic scenarios concerning the objective value and thecomputational demand of the optimization. Besides providing a more realisticsolution for the traffic management problem, the proposed multi-stage modelssignificantly decrease the optimization runtime.

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