Integrated optimization for passenger comfort-oriented train timetabling and passenger control strategy on an oversaturated metro line

• Published in: Engineering optimization Vol. 56; no. 10; pp. 1683 - 1706
• Main Authors: Zhang, Tong, Xu, Yan, Sun, Lishan, Chen, Yingda, He, Chongqi, Wang, Xinwei, Xiong, Jie
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.10.2024; Taylor & Francis Ltd
• Subjects: Adaptive algorithms; Adaptive control; Boarding; Congestion; density of platform passengers; generalized waiting time; Genetic algorithms; Heuristic methods; Metro line; Mixed integer; Nonlinear programming; Passenger comfort; passenger control strategy; Simulated annealing; train timetable; Travel demand
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2023.2283035

The excessive travel demand in the metro system during peak hours may cause platform congestion and reduce the comfort of the passengers. Thus, this article proposes an approach for integrally optimizing the train timetable and passenger control on an oversaturated metro line, to improve the comfort of passengers on the platform. Specifically, taking the short-term impact of the boarding/alighting passengers into account, a novel quantitative assessment method of the perceived waiting time, considering the impact of the platform passengers' density and passenger control strategy, named the generalized waiting time, is proposed. A mixed-integer nonlinear programming model is formulated to minimize the generalized waiting time, and a heuristic algorithm based on the adaptive simulated annealing genetic algorithm and the adaptive large neighbourhood search (ASAGA-ALNS) is designed to find high-quality solutions. The results of numerical experiments show that the method can alleviate platform congestion, while improving the comfort of waiting passengers.