Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Layout Optimization Method of Railway Transportation Route Based on Deep Convolution Neural Network

  • Cong, Qiao (School of International Education, Zhengzhou Railway Vocational & Technical College) ;
  • Qifeng, Gao (Dept. of Student's Management, Zhengzhou Asia-Europe Transportation Vocational College) ;
  • Huayan, Xing (International Department of Communication and Cooperation, Zhengzhou Railway Vocational & Technical College)
  • Received : 2022.05.30
  • Accepted : 2022.10.06
  • Published : 2023.02.28
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Abstract

To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.

Keywords

Acknowledgement

The research is supported by Zhengzhou Railway Vocational & Technical College 2021 School-level Educational Teaching Reform Research and Practice Project, "Research on the exploration of dynamic teaching and training model for internationalized composite talents," (No. 2021JG64).

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