## Cong Qiao , Qifeng Gao and Huayan Xing## |

Path layout No. | First station | Second stop | Third station | Fourth stop | Fifth stop |
---|---|---|---|---|---|

1 | 1 | 5 | 9 | 38 | 60 |

2 | 1 | 11 | 16 | 57 | 60 |

3 | 1 | 12 | 35 | 59 | 60 |

4 | 1 | 11 | 46 | 57 | 60 |

5 | 1 | 7 | 28 | 51 | 60 |

6 | 1 | 9 | 29 | 49 | 60 |

7 | 1 | 13 | 31 | 58 | 60 |

8 | 1 | 18 | 32 | 42 | 60 |

9 | 1 | 21 | 27 | 43 | 60 |

10 | 1 | 17 | 36 | 57 | 60 |

In order to prove the effectiveness of the proposed method, the prediction accuracy of the proposed method and the methods in [9] and [10] was comparatively analyzed. The statistical results are shown in Fig. 2.

From the experimental results in Fig. 2, it can be seen that when the number of iterations is different, the prediction error of different methods is very different, indicating that the number of iterations has a significant impact on the prediction error. The analysis of the experimental results in Fig. 2 shows that the more iterations, the better the prediction effect of different methods. The proposed method has higher prediction accuracy than the other two methods. When the iteration number is 900, the prediction accuracy of the proposed method can reach more than 90%.

To verify that the CNN optimized by ant colony algorithm has an effective application value for railway transportation optimization, MATLAB software platform was used to carry out simulation experiments. The data samples provided by the railway transportation enterprises were collected and analyzed, and the test data set was constructed. At the same time, the ant colony optimization CNN (ACO-CNN) and the particle swarm optimization CNN (PSO-CNN) proposed in this study were comparatively analyzed. The experimental results are shown in Fig. 3.

It can be seen from Fig. 3 that the PSO algorithm and ACO algorithm can both optimize the transportation distance and transportation time of 10 railway transportation routes. As a whole, ACO-CNN algorithm is better than PSO algorithm in optimization of transportation distance length and time consumption. In Fig. 3(a), the average distance of 10 sample data is 233.17 km, and the average distance after ACO optimization and PSO optimization is 209.36 km and 215.28 km, respectively. As shown in Fig. 3(b), the average time of 10 sample data is 3.314 hours, and the average time after ACO optimization and PSO optimization is 3.124 hours and 3.137 hours, respectively.

The deep CNN is used to optimize the layout of railway transportation routes, and the experimental results show that this method can effectively optimize the railway transportation line layout and obtain the optimal railway transportation line. However, at present, there are the following problems in China's railway transportation scheduling: in the railway transportation scheduling and command work, the dispatcher plays an important role in the scheduling work. In order to achieve efficient and safe trans¬portation dispatching and command, dispatchers must master comprehensive and professional dispat¬ching knowledge, understand the personnel, equipment and all aspects of the area under their jurisdiction, and do a good job in command. However, due to high working pressure, dispatchers usually lack of energy and do not seriously study the basic rules and regulations, which leads to the accumulation and stagnation of working capacity with the passage of time and the lack of necessary operators in the layout of railway transportation lines, resulting in the difficulty in maximizing the economic benefits.

This paper focuses on the layout optimization of railway transportation, applies deep CNN to the layout optimization of railway transportation routes, and evaluates its performance. The test results show that the proposed method can realize layout optimization of the railway transportation routes, and obtain optimal railway transportation route with high efficiency. Using this method to optimize the layout of railway transportation routes, the route with the shortest distance and the least time consumption can be obtained, which can maximize the economic benefits of railway transportation enterprises. Therefore, the proposed method has certain application value in the field of railway transportation route planning.

He was born in August 1987. He graduated from Vladivostok National University of Economics in 2011, majoring in international business. In 2013, he graduated from St. Petersburg National University of Economics, majoring in strategic management. In 2017, he received Ph.D from Ural State University of Railway Transport, majoring in transportation management. Now he works at Zhengzhou Railway Vocational and Technical College as an associate professor. He has published 6 academic articles and and participated in 9 scientific research projects.

He was born in August 1986. He graduated from Changzhi Medical College in 2010, majoring in applied psychology. In 2014, he received M.S. degreee from Henan University, majoring in basic psychology. Now, he works at Zhengzhou Railway Vocational and Technical College as an assistant. He has published 8 academic articles and participated in 7 scientific research projects.

She was born in July 1964. She graduated from Nanjing Railway Medical College in 1987, majoring in health. She received M.S. degree from Zhengzhou University of Railway Science and Technology in 2003. Now, she works at Zhengzhou Railway Vocational & Technical College as a professor. She has published 21 academic articles and participated in 17 scientific research projects.

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