Konferensartikel

This paper deals with robustness evaluation at station, and in particular for the train platforming problem (TPP). This problem consists in a platform and route assignment in station for each scheduled train. A classical robustness evaluation is simulation: simulated delays are injected on arriving and departing trains then propagated, and results are averaged on a large number of trials. A robust solution of the TPP aims to limit the average amount of secondary delays. However, a simulation framework at station is difficult to calibrate : it requires a realistic delays generator and an accurate operating rules modeling. This paper proposes an original simulation framework using classical statistical learning algorithms and calibration assessment methods to model simulation inputs. This methodology is applied on delay data to simulate delay propagation at station. It highlights the importance of delay calibration by showing that even slight miscalibration of inputs can lead to strong deviations in propagation results.

Simulation, platforming problem, Calibration, Machine learning, Delay Distribution