Optimizing and synchronizing timetable in an urban subway network with stop-skip strategy. (June 2022)

• Record Type: Journal Article
• Authors: Motvallian Naeini, Homa; Shafahi, Yousef; SafariTaherkhani, Mohammad
• Is Part Of: Journal of rail transport planning & management. Volume 22(2022)
• Publication Date: 2022-06
• Subjects: Timetabling -- Stop-skipping strategy -- Public transportation -- Lines synchronization; Railroads -- Periodicals; Railroads -- Planning -- Periodicals; Railroads -- Management -- Periodicals; Railroads; Railroads -- Management; Railroads -- Planning; Periodicals; 385.068
• DOI: 10.1016/j.jrtpm.2022.100301
• Languages: English
• ISSNs: 2210-9706

Abstract

Abstract: Stop-skipping and timetable synchronization are two effective strategies to reduce total passengers' travel time in a transit network for subway operation. However, the majority of studies conducted on the topic do not consider stop-skipping strategy and timetable synchronization simultaneously. Thus, this article proposes a mixed-integer programming model considering both strategies simultaneously. The model is based on passenger smart-card data concerning the trains' capacity to minimize total passengers' waiting time and in-vehicle time and maximize the number of passengers who successfully reach their destination in a specific study horizon. Since increasing the number of trains, stations, or the study horizon, exponentially increases the size of the problem, seeking efficient methods to solve real-sized problems is inevitable. Therefore, a heuristic algorithm based on a genetic algorithm (GA) was developed to solve the model. A hypothetical example was solved with GAMS (CPLEX) in order to evaluate the performance of both the model and the used algorithm. Then, the results were compared with the results of GA. Finally, a large-scale, real-life case study based on Tehran rail transit network was used to evaluate the proposed models in this study and the genetic algorithm approach. The results indicated that the proposed model reduced each passenger's travel time by approximately 4.78%, on average, and it also reduced each passenger's transfer waiting time by approximately 32.4%, on average in a peak hour. Finally, it maximized the number of passengers who reached their destination successfully in the considered study horizon. Highlights: Simultaneous use of Stop-skipping strategy and timetable synchronization in metro. Decreasing travel time of passengers using the metro system. Using genetic algorithm as a heuristic algorithm to solve big networks. Reducing passengers congestion at metro transfer stations. Policies and strategies that enhance the subway system's operation quality.