Last train timetabling optimization for minimizing passenger transfer failures in urban rail transit networks: A time period based approach

Quan Zhang, Xuan Li, Tao Yan, Lili Lu and Yang Shi

Physica A: Statistical Mechanics and its Applications, 2022, vol. 605, issue C

Abstract: In order to improve the time accessibility of URT networks for passengers traveling in the late evening, it is more important to coordinate the train timetables among different lines than to prolong the service time alone. This paper focuses on train timetabling optimization for the last time period of daily URT service. Different from the existing last train timetabling model optimizing the last train timetables alone and considering only the last train passengers, this study expands the optimizing object to the train timetables in the entire period and broaden the beneficiary to all the transfer passengers in the period. An optimization model is developed for minimizing the total passenger transfer failures in such a way that the departure and arrival times of the last trains and non-last trains are made integrated coordination. To solve for large-scale URT networks, we design an ABC algorithm that is verified computationally efficient. A method evaluating the impact of small last train delays on the optimized timetables is proposed to help URT operators in identifying the most “dangerous” delay locations and the most vulnerable transfer relationships. Finally, a case study on Shanghai URT network demonstrates that the proposed period-based train timetabling approach is effective in reducing passenger transfer failures at both the network level and the station level. Comparison analysis shows that the proposed model significantly outperforms the existing last train timetabling model in improving transfer accessibility as well as train connections.

Keywords: Urban rail transit network; Train timetabling; The last time period of daily service; Artificial bee colony algorithm; Delay impact evaluation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006665

DOI: 10.1016/j.physa.2022.128071

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