Optimizing urban rail timetable under time-dependent demand and oversaturated conditions

• Published in: Transportation research. Part C, Emerging technologies Vol. 36; pp. 212 - 230
• Main Authors: Niu, Huimin, Zhou, Xuesong
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier India Pvt Ltd 01.11.2013; Elsevier
• Subjects: Applied sciences; Exact sciences and technology; Genetic algorithm; Ground, air and sea transportation, marine construction; Oversaturated condition; Railway transportation and traffic; Time-dependent demand; Train timetable; Transit service optimization; Transportation planning, management and economics; Urban rail line; Time-dependent demand; Genetic algorithm; Oversaturated condition; Urban rail line; Transit service optimization; Train timetable; Transport demand; Supersaturation; Scheduling; Running of trains; Modeling; Optimization; Integer programming; Time dependence; Urban transportation; Example; Timetabling problem; Numerical simulation; Strategy; Rail transportation
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2013.08.016

•Develop integer programming models to design train timetables in a heavily congested urban rail corridor.•Analytically calculate effective passenger loading time periods under dynamic demand patterns.•Calculate time-dependent waiting times under oversaturated conditions.•Propose gradient-based and genetic algorithms for optimizing timetables. This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. Using cumulative input–output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. The effectiveness of the proposed model and algorithm are evaluated using a real-world data set.