Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations

• Published in: Railway Engineering Science (Online) Vol. 31; no. 4; pp. 351 - 369
• Main Authors: Pang, Zishuai, Wang, Liwen, Wang, Shengjie, Li, Li, Peng, Qiyuan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Automotive Engineering; Averaging mechanism; Buses; Delay; Dwell time; Dynamic smoothing; Engineering; Fluctuations; Foundations; Geoengineering; Headways; High speed rail; Hydraulics; Influence; Machine learning; Passenger flow; Passengers; Predictions; Railway stations; Real time; Regional/Spatial Science; Smoothing; Traffic; Train operations; Trains; Vehicles; Train operations; Averaging mechanism; Dynamic smoothing; Passenger flow; Dwell time
• ISSN: 2662-4745; 2662-4753
• DOI: 10.1007/s40534-023-00311-7

Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences.