Zhou, Jingyang (Beijing Jiaotong University), Wu, Xueliang (Beijing Mass Transit Railway Operation Corporation Limited), Shang, Du (Beijing Jiaotong University), Su, Shuai (Beijing Jiaotong university)

Train Operating Schedule Adjustment Model Based on Rain and Snow Conditions

Scheduled for presentation during the Poster Session "Railway systems and applications" (ThBT13), Thursday, September 26, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Inclement weather, such as rain and snow, can significantly affect the operation of urban rail systems, particularly in open-air sections of the track. In order to ensure the safety of train operation, it is necessary to organize the train to run at a limited speed in the open-air section. Train timetable is the basis of urban rail transportation organization, and trains run according to the given planning train timetable under normal conditions. However, the imposition of speed restrictions during adverse weather can result in delays and subsequen disruptions to the schedule. To address this problem, combined with the objectives and characteristics of operation and scheduling under rainy and snowy weather conditions, the train is adjusted by comprehensively adopting adjustment means such as early train departure, shortening stopping time, additional reserve trains, intermediate turnarounds and compression of the turnaround time of the underground line, and a train constraint model under rainy and snowy conditions has been established to take into account the decision-making variable sand constraints related to the training. The main objective under rain and snow conditions is to maintain capacity as much as possible, so the model aims to minimize train timetable deviations and minimize the number of stations with service cancellations to model the adjustment of train operations under rain and snow conditions and derive valid inequalities based on the model characteristics. The model is then transformed into a mixed-integer linear programming (MILP) model, which is solved using effective inequalities combined with the branchand-bound method of SCIP. Finally, the validity of the model is verified by an arithmetic example.