Y.N. Wei, X.Z. Guo

Pages: 139-150

Abstract
The existing planning of the rapid rail transit network has resulted in fewer public transfer stations and longer travel times due to the lack of consideration for actual travel needs. In response to this issue, this article proposes a urban rapid rail transit network planning method that considers travel demand conditions. Firstly, extract the characteristics of urban rapid rail transit; Secondly, considering the travel demand conditions, divide the traffic pressure on the target route into regions; Again, use genetic algorithms to evaluate the suitability of the best transportation route; Finally, the immune cloning algorithm is used to optimize the line network planning. The experimental results show that the number of transfer stations planned in this article is relatively large, with a single station travel time of less than 42 minutes and a total travel time of 76.65 minutes for multiple stations. The travel time is relatively short, and the travel efficiency is high, and the planning effect is good.
Keywords: rapid rail transit; line network planning; travel demand; genetic algorithm; immune clonal algorithm