Passenger and energy-saving oriented train timetable and stop plan synchronization optimization model
The stability of train timetables plays an important role in ensuring the safety and punctuality of train operations. Meanwhile, a reasonable stop plan has a significant effect on passenger and energy consumption. Existing research has focused exclusively on adjusting timetables after delays occur, or on adjusting them without considering stop plans. By contrast, in this study, the authors developed a train timetable and stop plan synchronization optimization model that is passenger and energy-saving oriented. The following objectives were considered for minimization: probability of train delays, energy consumption, and travel time of the trains. A parallelism selection genetic algorithm was designed to solve the model. The section of high-speed railway from Nanjing South to Shanghai Hongqiao was utilized as a case study to evaluate the proposed model. The results showed that, while meeting passenger demand, the proposed optimization approach increased the punctuality rate by 12.55% and decreased the energy consumption by 8.16%.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
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Supplemental Notes:
- © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Xie, Jia
- Zhang, Jie
- Sun, Keyang
- Ni, Shaoquan
- Chen, Dingjun
- Publication Date: 2021-9
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 102975
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Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 98
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Energy consumption; High speed rail; Optimization; Passenger service; Timetables
- Subject Areas: Energy; Passenger Transportation; Railroads;
Filing Info
- Accession Number: 01781732
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 20 2021 2:52PM