Real-time railway traffic management optimization and imperfect information: preliminary studies (original) (raw)
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The railway timetables are designed in an optimal manner to maximize the capacity usage of the infrastructure concerning different objectives besides avoiding conflicts. The realtime railway traffic management problem occurs when the preplanned 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 mixedinteger 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.
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Rail transportation helps to reach the global climate targets because it is characterized by low emission. The passenger and freight volumes on the railway increase yearly in line with EU targets. However, delays of passenger and freight trains decrease the punctuality and the reliability of the railway sector and the development of the infrastructure is not enough to increase the average speed of trains. Delays mean cost to the passengers, railway operators, infrastructure managers, and all railway undertakers. Therefore, the reason for the most significant optimization target is to minimize delays. In this paper, a possible solution has been described to solve the real-time railway traffic management problems by applying a mixed-integer linear programming approach. For validation of the research result, one simplified case study has been presented. Based on the result, the presented solution can provide effective support to dispatchers in solving real-time traffic management probl...
Real-time train routing and scheduling through mixed integer linear programming: Heuristic approach
In railway traffic management, when an unexpected event perturbs the system, finding an effective train routing and scheduling in real-time is a key issues. Making the right routing and scheduling decisions may have a great impact on the efficiency of the system in terms of delay propagation. However, the time available for making these decisions is quite short: in few minutes a viable set of routes and schedules must be delivered to the dispatching system. In this paper, we assess the performance of a mixed integer linear programming (MILP) formulation exploited as a heuristic approach: we seek for the best feasible solution given a limited and predefined computation time. We run an experimental analysis on instances representing traffic in the Lille Flandres station, France. The results show that the approach tested is very promising, often finding the optimal solution to the instances tackled. Moreover, we show how the performance can be improved by tuning the parameters of the MILP solver.
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Railway traffic is often perturbed by unexpected events causing delays, which may greatly propagate. Nowadays, dispatchers deal with delays trying to limit this propagation with scarce decision support tools. RECIFE-MILP is an optimization algorithm which may be used to support dispatchers' decisions. In this paper, we illustrate the analysis performed in collaboration with the French infrastructure manager (SNCF R eseau) to assess the actual impact of the application of optimization in real-time railway traffic management. We perform a twofold experimental analysis on two French complex junctions characterized by intense mixed (passenger and freight) traffic. On the one hand, we assess this impact on scenarios specifically identified as relevant by SNCF R eseau. On the other hand, we tackle actually occurred scenarios and we compare the decisions made by RECIFE-MILP with those made by the dispatchers who actually faced the perturbation. These experiments show through simulation that the optimization may remarkably improve the way traffic is managed.
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