Software tools using computer simulations are frequently used in the research and optimization of railway transport systems. Such simulations serve to examine different railway traffic scenarios (which typically reflect different timetables and railway infrastructure configurations). During the simulation experiments, it is necessary, among other things, to solve tasks related to the determination of track routes along which individual trains or parts of train sets are moved. Many simulation tools require the basic and alternative permissible track routes to be manually specified before starting the simulations, which is a relatively tedious and time-consuming process. Classical graph algorithms cannot be applied to solve the problem of automatic calculation of the routes because they are unable to take into account the length of the object being moved or recognise changes in the direction of its movement. This article presents original innovative algorithms focused on automated dynamic search of track routes (applying an appropriate optimization criterion), which is performed during simulation experiments within simulators working at the mesoscopic level of detail. The algorithms are based on a mathematical model (represented by a specifically designed weighted digraph) that appropriately reflects the actual track infrastructure. The dynamic calculation of each specific track route for a train or a group of railway vehicles considers both the total train set length and the current railway infrastructure occupancy, including blocked parts of the infrastructure due to intervention of the interlocking system. In addition, the places where the train set movement direction is changed can be identified on each route found. Applications of the algorithms and of the mathematical model of the track layout are demonstrated on a model track infrastructure.
CITATION STYLE
Kavička, A., & Krýže, P. (2021). Dynamic Automated Search of Shunting Routes within Mesoscopic Rail-Traffic Simulators. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/8840516
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