Modeling train route decisions during track works

Abstract

To better understand the choice behavior of train route schedulers and to predict their choices for optimizing the annual construction schedule, prospective data for 2020 on train route decisions are analyzed using discrete choice models and machine learning classifiers. The choice alternatives include (i) partial cancellation of the train schedule at the start, (ii) in the middle or (iii) at the end of the itinerary of the train service, (iv) detour and (v) delay/ahead of time, and are modeled using 39 train-, construction site-, and infrastructure variables. The top nine attributes account for about 80% of variable importance, including the travel time from the departure station to the construction site, total or line closure, travel time from the construction site to the terminus, length of the train and effective line capacity.

The models are tested for 2021 and 2022 to verify whether they can be used to forecast choices in the following years. While Random Forest performs best in terms of prediction accuracy (2021: 60.8%; 2022: 58.6%), the improvements of about 6%-points compared to the Mixed Logit model are modest. Results indicate that a substantial amount of unobserved construction site heterogeneity is present, which Random Forest cannot capture either.