Train timetabling of loop line for minimizing passenger waiting time and platform congestion

Loop lines are critical transfer hubs in urban rail transit, where poorly coordinated timetables can increase passenger waiting time and platform congestion. This study proposes a timetable coordination model for a loop line and its connecting lines to jointly reduce passenger waiting time and mitigate platform congestion. Passenger accumulation is characterized by explicitly modeling boarding, alighting, and transfer processes. A customized adaptive large neighborhood search algorithm with problem-specific destroy and repair operators is developed to solve the resulting large-scale problem. Numerical experiments on the Beijing Metro show that minimizing waiting time alone reduces waiting time by 6.8% but increases maximum platform passenger density by 16.6%. In contrast, the proposed method reduces maximum platform passenger density by 9.0% and passenger waiting time by 4.7%, demonstrating its effectiveness in balancing service efficiency and platform congestion mitigation.

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Publication Details

Journal
Transportation Letters
Published
2026-11-02
DOI
https://doi.org/10.1080/19427867.2026.2750871
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
0.00

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article

Train timetabling of loop line for minimizing passenger waiting time and platform congestion

Fang Wen, Ninghai Li, Junsheng Huang, Yun Li et al.
1 links
Transportation Letters
Railway Systems and Energy Efficiency
article

Train timetabling of loop line for minimizing passenger waiting time and platform congestion

Fang Wen, Ninghai Li, Junsheng Huang, Yun Li, Yao Chen, Yun Bai
article en

Abstract

Loop lines are critical transfer hubs in urban rail transit, where poorly coordinated timetables can increase passenger waiting time and platform congestion. This study proposes a timetable coordination model for a loop line and its connecting lines to jointly reduce passenger waiting time and mitigate platform congestion. Passenger accumulation is characterized by explicitly modeling boarding, alighting, and transfer processes. A customized adaptive large neighborhood search algorithm with problem-specific destroy and repair operators is developed to solve the resulting large-scale problem. Numerical experiments on the Beijing Metro show that minimizing waiting time alone reduces waiting time by 6.8% but increases maximum platform passenger density by 16.6%. In contrast, the proposed method reduces maximum platform passenger density by 9.0% and passenger waiting time by 4.7%, demonstrating its effectiveness in balancing service efficiency and platform congestion mitigation.

Transportation Letters
Beijing Jiaotong University (CN), Guangdong Polytechnic Normal University (CN), Shenzhen Technology University (CN)
National Natural Science Foundation of China
Sustainable cities and communities, Industry, innovation and infrastructure
Openalex Percentile: Top 16%
Railway Systems and Energy Efficiency
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