PhD project: Robust train trajectory optimization

In cooperation with the Dutch Railways (NS), Alex Cunillera works in this PhD research on robust train trajectory optimization. Even two trains of the same model running on the same line show significant differences in their dynamics. This might be due to different passenger loads, weather, fault history, driving style of the train driver, etc. Moreover, there are uncertainties in the track data that may also have a strong influence on the train operation. This research focuses on determining the uncertainties and stochastics of these variations and developing methods to compute robust train trajectories that optimize the energy consumption and minimize delays in the presence of the mentioned variations.

Project contributions (ongoing):

Papers:

Train motion model calibration: research agenda and practical recommendations (ITSC 2022) 

Real-time train motion parameter estimation using an Unscented Kalman Filter (Transportation Research Part C) 

Train trajectory optimization under parametric uncertainty and roubst maximum principle analysis (COIA 2022)

Presentations: 

Real-time train motion parameter estimation using an Unscented Kalman Filter (RailBeijing 2021)

Train motion model calibration: research agenda and practical recommendations (ITSC 2022)

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