Highlights Route conflicts at multi-line stations are considered from a microscopic train operation view. Arrival/departure route data is treated as textual information and processed by word embedding technique. The consideration of the route conflicts is proven to substantially improve the model performance. The model exhibits higher predictive accuracy than existing models and robust performance at different multi-line stations.
Abstract Multi-line stations (MLSs) are the intersections of different railway lines; they are crucial for delay propagation in railway networks. Therefore, the precise prediction of train arrival delays at the MLSs can efficiently support train operation rescheduling plans and reduce delay propagation in the railway network. The arrival routes of trains at the MLSs are critical factors for managing train arrival delays, since there may be latent route conflicts with forward arrival/departure trains. However, route conflicts will not occur at single-line stations (SLSs) that are traversed by only one railway line. Existing train delay prediction studies have considered the ways that trains arrive at/depart from stations as black boxes, but have not considered the latent route conflicts from a microscopic view. This study considers the arrival routes of predicted trains and route conflicts with forward trains, for contemplating the gap (not considering the route conflicts from other railway lines) in the existing studies. The influencing factors are separated into three categories according to the data attributes, namely, route-related variables, delay-related variables, and environment-related variables. Then, an architecture called LLCF-net is proposed, with a one-dimensional convolutional neural network (CNN) block for route-related variables, two long short-term memory (LSTM) networks for delay-related variables, and a fully connected neural network (FCNN) block for environment-related variables. Compared with the methods in exiting studies, this architecture showed the best performance for both two MLSs—GuangzhouSouth(GZS) and ChangshaSouth (CSS)—on the Chinese high-speed railway network, regardless of the consideration of route-related variables. In addition, LLCF-net is proven to have a strong predictive effectiveness and a robust performance for different delay lengths.
Prediction of train arrival delays considering route conflicts at multi-line stations
2022-02-05
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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