Route planning model of rail transit network facing the railway freight transport deadline

Abstract

The railway freight period seriously affected the overall operating efficiency of the railway network. As a complex dynamic system of the Internet of Things and Intelligent Transportation System, rail transit networks are currently mainly based on static network index analysis. This paper compares the results of CIGA with the optimal solution of the mixed-integer linear programming model based on small-scale test cases to test the optimality of CIGA. Then three meta-heuristic algorithms that have good results in solving classic FSGS problems are selected, and the results of CIGA are compared with the three algorithms based on large-scale test cases to test the effectiveness of CIGA. This paper takes the basic data of railway routes in our country as a reference and expands its format. All algorithms in the experiment are implemented using Matlab language programming. The results of the study show that the intermediate values of the five important intervals determined by the interval between values are between 11.555 and 18.40%, indicating that these five intervals can individually affect 11.55 to 18.40% of the shortest paths in the railway track network. The efficiency can have a greater impact. There are five important intervals determined by the proportion of goods affected by the interval. The proportion of goods individually affected by each interval is between 12.24 and 13.96%, and the corresponding cargo transportation volume affected is 42630 to 48,621. These intervals have an impact on cargo transportation.