A grid planning method for large urban rail transit using hybrid genetic algorithm

In response to the shortcomings of existing rail transit grid planning methods such as longer route lengths and longer travel times, this paper applies a hybrid genetic algorithm to design a large-scale urban rail transit grid planning method. Firstly, the transportation efficiency of the transportation grid is taken as the main objective function, and genetic algorithm coding is adopted, Then, a fitness function is designed to optimize the objective function through crossover, mutation operations, and elite retention strategies, Finally, the immune clone algorithm is introduced to obtain the final grid planning results for large-scale urban rail transit. Through experiments, it has been proven that the total length of the line in this article is 118.37km, with a total number of 56 stations. The total length of the line is the shortest, and the travel time is always less than 42min, which has high travel efficiency and good planning effect.