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.

铁路货运期严重影响了铁路网的整体运营效率。轨道交通网络作为物联网和智能交通系统的复杂动态系统，目前主要基于静态网络指标分析。本文将CIGA的结果与基于小规模测试案例的混合整数线性规划模型的最优解进行比较，以检验CIGA的最优性。然后选择了三种在解决经典FSGS问题上效果良好的元启发式算法，并将CIGA的结果与基于大规模测试案例的三种算法进行比较，以测试CIGA的有效性。本文以我国铁路路线的基本数据为参考，并扩展了其格式。实验中的所有算法均使用Matlab语言编程实现。研究结果表明，由五个重要间隔确定的中间值介于11.555和18.40％之间，表明这五个间隔可以分别影响铁路轨道网中最短路径的11.55％至18.40％。效率会产生更大的影响。有五个重要的时间间隔由受该时间间隔影响的商品比例确定。每个间隔单独影响的货物比例在12.24％至13.96％之间，相应的受影响货物运输量为42630至48,621件。这些间隔对货物运输有影响。研究结果表明，由五个重要间隔确定的中间值介于11.555和18.40％之间，表明这五个间隔可以分别影响铁路轨道网中最短路径的11.55％至18.40％。效率会产生更大的影响。有五个重要的时间间隔由受该时间间隔影响的商品比例确定。每个间隔单独影响的货物比例在12.24％至13.96％之间，相应的受影响货物运输量为42630至48,621件。这些间隔对货物运输有影响。研究结果表明，由五个重要间隔确定的中间值介于11.555和18.40％之间，表明这五个间隔可以分别影响铁路轨道网中最短路径的11.55％至18.40％。效率会产生更大的影响。有五个重要的时间间隔由受该时间间隔影响的商品比例确定。每个间隔单独影响的货物比例在12.24％至13.96％之间，相应的受影响货物运输量为42630至48,621件。这些间隔对货物运输有影响。效率会产生更大的影响。有五个重要的时间间隔由受该时间间隔影响的商品比例确定。每个间隔单独影响的货物比例在12.24％至13.96％之间，相应的受影响货物运输量为42630至48,621件。这些间隔对货物运输有影响。效率会产生更大的影响。有五个重要的时间间隔由受该时间间隔影响的商品比例确定。每个间隔单独影响的货物比例在12.24％至13.96％之间，相应的受影响货物运输量为42630至48,621件。这些间隔对货物运输有影响。