Integrated optimization of train route plan and timetable with dynamic demand for the urban rail transit line. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Yang, Ruixia; Han, Baoming; Zhang, Qi; Han, Zhenyu; Long, Yuxuan
• Is Part Of: Transportmetrica. Volume 11:Issue 1(2023)
• Page Start: 93
• Page End: 126
• Publication Date: 2023-12-31
• Subjects: Train timetable -- train route plan -- integrated optimization -- Improved NSGA-II -- dynamic demand; Transportation -- Mathematical models -- Periodicals; 388.015118
• DOI: 10.1080/21680566.2022.2040064
• Languages: English
• ISSNs: 2168-0566

Abstract

ABSTRACT: In the urban rail transit system, train timetabling according to the dynamic passenger demand is important to offer high-quality service. Timetable optimization with the predetermined train route plan cannot synchronously consider the spatial and temporal characteristics of demand. Based on the Automated Fare Collection (AFC) data, this study proposes the integrated optimization (IO) to make route plan and timetable. A mixed-integer nonlinear programming (MINLP) model is formulated with the aim of reducing passenger penalty travel time and operation penalty cost. An improved non-dominated sorted genetic algorithm-II (Improved NSGA-II) is applied to deal with the bi-objective problem. A numerical experiment is used to prove the effectiveness and efficiency of the model and the algorithm. Then, the IO approach is applied in a bi-directional urban rail transit line. The results show that the IO performs the best compared with two other feasible strategies, namely the current optimization (CO) and phased optimization (PO).