Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan
The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/3897353 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850232458556997632 |
|---|---|
| author | Lianbo Deng Qi Peng Li Cai Junhao Zeng Nava Raj Bhatt |
| author_facet | Lianbo Deng Qi Peng Li Cai Junhao Zeng Nava Raj Bhatt |
| author_sort | Lianbo Deng |
| collection | DOAJ |
| description | The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of “operating by the flow” and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values. |
| format | Article |
| id | doaj-art-0a83ce815d9a4002acd3691c24fc91ec |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0a83ce815d9a4002acd3691c24fc91ec2025-08-20T02:03:12ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/3897353Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation PlanLianbo Deng0Qi Peng1Li Cai2Junhao Zeng3Nava Raj Bhatt4School of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringThe Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of “operating by the flow” and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values.http://dx.doi.org/10.1155/2023/3897353 |
| spellingShingle | Lianbo Deng Qi Peng Li Cai Junhao Zeng Nava Raj Bhatt Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan Journal of Advanced Transportation |
| title | Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan |
| title_full | Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan |
| title_fullStr | Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan |
| title_full_unstemmed | Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan |
| title_short | Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan |
| title_sort | multiobjective collaborative optimization method for the urban rail multirouting train operation plan |
| url | http://dx.doi.org/10.1155/2023/3897353 |
| work_keys_str_mv | AT lianbodeng multiobjectivecollaborativeoptimizationmethodfortheurbanrailmultiroutingtrainoperationplan AT qipeng multiobjectivecollaborativeoptimizationmethodfortheurbanrailmultiroutingtrainoperationplan AT licai multiobjectivecollaborativeoptimizationmethodfortheurbanrailmultiroutingtrainoperationplan AT junhaozeng multiobjectivecollaborativeoptimizationmethodfortheurbanrailmultiroutingtrainoperationplan AT navarajbhatt multiobjectivecollaborativeoptimizationmethodfortheurbanrailmultiroutingtrainoperationplan |