Section: Application Domains

Forecast of dwell time during train parking at stations

This is a Cifre PhD in collaboration with SNCF.

One of the factors in the punctuality of trains in dense areas (and management crisis in the event of an incident on a line) is the respect of both the travel time between two stations and the parking time in a station. These depend, among other things, on the train, its mission, the schedule, the instantaneous charge, and the configuration of the platform or station. Preliminary internal studies at SNCF have shown that the problem is complex. From a dataset concerning line E of the Transilien in Paris, we will address prediction (machine learning) and modeling (statistics): (1) construct a model of station-hours, station-hours-type of train, by example using co-clustering techniques; (2) study the correlations between the number of passengers (load), up and down flows, and parking times, and possibly other variables to be defined; (3) model the flows or loads (within the same station, or the same train) as a stochastic process; (4) develop a realistic digital simulator of passenger flows and test different scenarios of incidents and resolution, in order to propose effective solutions.