Search

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on how to minimize the total passenger travel time cost by computing and adjusting skip-sop patterns with given time-varying origin-to-destination passenger demand matrices. A bi-objective nonlinear integer programming model with linear constraints is proposed to precisely formulate the total travel time cost and operating cost under minute-dependent demand from the different origin–destination pairs. The proposed model is implemented by using the genetic algorithm with the idea point optimization solvers, and we show its effectiveness on the real world instance of Guangzhou Metro Line 8.

Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. This paper explores the optimization to train timetable and skip-stop plans that aims to minimize the total passenger waiting time and station crowding. The problem is formulated as a mixed-integer non-linear programming model. To effectively address the complexity of the model, a decomposition and approximate dynamic programming approach is designed to convert the original network-level problem into a series of small-scale subproblems, one for each operating line, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and algorithm are demonstrated on two case networks: a small-scale synthetic network of three metro lines and a real-world network based on Beijing metro. The computational results illustrate that the proposed strategy to generate train timetables and skip-stop plans can effectively reduce passenger waiting time and station crowing. The proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional heuristic algorithms, such as genetic algorithm and simulated annealing algorithm for large-scale networks. • The optimization of train timetable and skip-stop plans of metro networks is considered. • A mixed-integer nonlinear programming model is formulated for this problem. • A decomposition and approximate dynamic programming approach is designed. • The optimized strategy reduces passenger waiting time and station crowing. [ABSTRACT FROM AUTHOR]

In the case of a metro disruption, the planned timetable cannot be operated and a large number of passengers are left stranded in the stations. When the disruption is over, some stations may be skipped in the recovery period, which speeds up the circulation of trains and makes the number of stranded passengers reduce faster. Considering an over-crowded and time-dependent passenger flow, this paper proposes an optimization model to reschedule a metro line. To achieve a balance between theoretical validity and computational convenience, the optimization model is decomposed, and an iterative algorithm is proposed to solve the model. Numerical experiments based on the Beijing Metro are carried out, the results of which verify the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrices. Given predetermined train skip-stop patterns, a unified quadratic integer programming model with linear constraints is developed to jointly synchronize effective passenger loading time windows and train arrival and departure times at each station. A set of quadratic and quasi-quadratic objective functions are proposed to precisely formulate the total waiting time under both minute-dependent demand and hour-dependent demand volumes from different origin–destination pairs. We construct mathematically rigorous and algorithmically tractable nonlinear mixed integer programming models for both real-time scheduling and medium-term planning applications. The proposed models are implemented using general purpose high-level optimization solvers, and the model effectiveness is further examined through numerical experiments of real-world rail train timetabling test cases. [ABSTRACT FROM AUTHOR]