The development of high-speed railways (HSR) in China has attracted a large number of passengers from highway and aviation to railways due to their comfort and high speed. In this case, HSR passenger transportation can improve the operating income by optimizing the ticket allocation. Here, we propose an optimization method of multiclass price railway passenger transport ticket allocation under high passenger demand. First, for the “censored data” problem in the railway passenger demand forecast, we constructed an unconstrained model of railway passenger demand and solved the unconstrained demand through an expectation-maximization algorithm. Then, on this basis, we use gray neural networks (GNNs) to predict the passenger demand of different origins and destinations (ODs), and according to the prediction results, we propose two ticket allocation methods based on operation and capacity control: accurate predivided model and fuzzy predivided model. And we solve this problem by constructing a particle swarm optimization algorithm. Lastly, we use examples to prove that the proposed ticket allocation method can meet the passengers’ needs and have good economic benefits.
Document type: Article
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