- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LI Bing, LI Yang, XUAN Hua. Train Operation Adjustment Optimization Considering Transfer under Influence of Railway Line Fault[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240348
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Because of the train operation adjustment considering transfer under the railway line faults, minimizing the total train knock-on delay time and the number of passengers failing to transfer caused by railway line faults was taken as two objectives, and an optimization model for train operation adjustment under the influence of railway line faults was constructed. In light of the model as a large-scale hybrid integer programming model, an improved particle swarm based on Gaussian walk (IPS&GW) was designed. Road network composed of some upward-direction sections of the Beijing–Guangzhou Railway and downward-direction sections of the Zhengzhou–Xuzhou Railway was used to form a testing scenario. The proposed IPS&GW algorithm was used to solve the optimization model for train operation adjustment based on different train operation recovery strategies (strategy Ⅰ–strategy Ⅳ). The results show that compared to strategy Ⅰ, when the disturbance duration of railway line fault ranges from 10 to 30 minutes, the decreasing ratio of train knock-on delay time based on strategies Ⅱ, Ⅲ, and Ⅳ is respectively in the range from 82.5% to 86.6%, from 70.5% to 81.49%, and from 55.8% to 58.7%. The decreasing ratio of the total number of stranded passengers is respectively in the range from 28.3% to 39.1%, from 57.3% to 61.9%, and from 88.4% to 89.4%. Under different disturbance scenarios, a significant decrease in the number of passengers failing to transfer can be achieved by increasing the total train delay time by a certain amount. Finally, the proposed IPS&GW algorithm is compared with the traditional particle swarm optimization (PSO) algorithm. The former shows an advantage in the convergence speed of the objective function, so that the scheduled operation requirements for the train can be met, and the number of stranded passengers in the transfer hub stations is reduced.
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