Abstract
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.
Highlights
China’s high-speed railway (HSR) is developing rapidly
E first part of the paper puts forward the necessity of introducing revenue management and analyzes the feasibility of introducing revenue management in HSR passenger transport from the adaptive perspective and social environment. e second part reviews the literature on unconstrained demand and railway revenue management. e third part establishes the corresponding model according to the demand prediction and capacity control methods of revenue management. e fourth part, with the WuhanGuangzhou HSR, proves our method can bring economic
Another processing method considers that the demand observed by the system is a real demand, without considering the predetermined limitations of the system, that is, unconstrained processing. erefore, we propose an HSR ticket allocation optimization method based on the unconstrained processing of censored data to improve the accuracy of prediction
Summary
China’s high-speed railway (HSR) is developing rapidly. China HSR has high fixed construction costs and low passenger revenue. Erefore, HSR companies need to use management methods to optimize the allocation of existing transportation resources, attract more passengers, and increase revenue without increasing costs. E existing historical data cannot fully reflect the actual demand of passengers, forming an unlimited demand forecast. Cross proposed that revenue management is a method to maximize economic benefits by subdividing the market and dynamically forecasting demand [1]. The unrestrained demand forecast can infer the unrestrained demand data according to the Journal of Advanced Transportation. Erefore, the introduction of revenue management and unstrained demand forecasting into China’s HSR passenger transport market can achieve the rational allocation of existing high-speed rail transport resources and capacity. E first part of the paper puts forward the necessity of introducing revenue management and analyzes the feasibility of introducing revenue management in HSR passenger transport from the adaptive perspective and social environment. e second part reviews the literature on unconstrained demand and railway revenue management. e third part establishes the corresponding model according to the demand prediction and capacity control methods of revenue management. e fourth part, with the WuhanGuangzhou HSR, proves our method can bring economic
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