Increasing the precision in timetable planning is a key success factor for all infrastructure managers, since it allows us to minimize delay propagation without reducing usable capacity. Since most running time calculation models are based on standard and deterministic parameters an imprecision is implicitly included, which has to be compensated by running time supplements. At the same time, GPS or even more precise trackings are continuously stored in the event recorders of most European trains. Unfortunately, this large amount of data is normally stored but not used except for failure and maintenance management. To consider real running time variability in running time calculation, an approach has been developed, which allows us to calibrate a performance factor for each motion phase. Given the standard motion equation of a train, and a mesoscopic model of the line, the tool uses a simulated annealing optimisation algorithm to find the best regression between calculated and measured instant speed. To increase precision, the motion is divided into four phases: acceleration, braking at stops, braking for speed reductions/signals and cruising. By performing the procedure over a number of train runnings, a distribution of each performance parameter is obtained. Once the infrastructure model is defined and the trackings are imported, the procedure is completely automated. The approach can be used in both stochastic simulation models and as a basis for advanced timetable planning tools, where stochastic instead of deterministic running times are used. The tool has been tested in the north-eastern part of Italy as input for both running time calculation and microscopic simulation.

An algorithm for the calibration of running time calculation on the basis of GPS data

de FABRIS, STEFANO;LONGO, GIOVANNI;MEDEOSSI, GIORGIO
2011-01-01

Abstract

Increasing the precision in timetable planning is a key success factor for all infrastructure managers, since it allows us to minimize delay propagation without reducing usable capacity. Since most running time calculation models are based on standard and deterministic parameters an imprecision is implicitly included, which has to be compensated by running time supplements. At the same time, GPS or even more precise trackings are continuously stored in the event recorders of most European trains. Unfortunately, this large amount of data is normally stored but not used except for failure and maintenance management. To consider real running time variability in running time calculation, an approach has been developed, which allows us to calibrate a performance factor for each motion phase. Given the standard motion equation of a train, and a mesoscopic model of the line, the tool uses a simulated annealing optimisation algorithm to find the best regression between calculated and measured instant speed. To increase precision, the motion is divided into four phases: acceleration, braking at stops, braking for speed reductions/signals and cruising. By performing the procedure over a number of train runnings, a distribution of each performance parameter is obtained. Once the infrastructure model is defined and the trackings are imported, the procedure is completely automated. The approach can be used in both stochastic simulation models and as a basis for advanced timetable planning tools, where stochastic instead of deterministic running times are used. The tool has been tested in the north-eastern part of Italy as input for both running time calculation and microscopic simulation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2463729
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