Optimizing urban rail timetable under time-dependent demand and oversaturated conditions
•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...
Uloženo v:
| Vydáno v: | Transportation research. Part C, Emerging technologies Ročník 36; s. 212 - 230 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Kidlington
Elsevier India Pvt Ltd
01.11.2013
Elsevier |
| Témata: | |
| ISSN: | 0968-090X, 1879-2359 |
| On-line přístup: | Získat plný text |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Niu, Huimin Zhou, Xuesong |
| Author_xml | – sequence: 1 givenname: Huimin surname: Niu fullname: Niu, Huimin email: hmniu@mail.lzjtu.cn organization: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China – sequence: 2 givenname: Xuesong surname: Zhou fullname: Zhou, Xuesong email: xzhou74@asu.edu organization: School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, United States |
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| Keywords | 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 |
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