Analysis of Passengers’ Tickets Pre-booking Behavior in High-speed Railway Based on Market Segmentation

Chinese high-speed railway has gradually formed into a big network, and passengers pay more attention to the quality of the transport services provided by railway operation departments, so accurately obtaining passengers’ tickets pre-booking behavior characteristics becomes the key to improve high-speed transport services. Since there is no research on classifying and describing different types of passengers using both passenger survey data and ticket data in the current study. In this paper, we pay more attention to the use of 2 kinds of data. Firstly, market segmentation of high-speed rail passengers is proposed, which is based on K-Means Cluster analysis and using passenger survey data. Then, this paper draws lessons from Naive Bayes Classifier, the ticket data are classified according to the result of market segmentation. And a passengers’ tickets pre-booking behavior model based on Multi-Logit Model is established. Finally, through the analysis of specific parameters, the tickets pre-booking preferences of four different types of passengers (family tourism market, personal visiting market, student market and business market) are classified and described. Using the model, passenger flow forecasting can be realized for different trains. The results can be used for designing high-speed railway products.

High-speed Railway, Market Segmentation, K-Means Cluster, Passenger Choice Behavior, Logit Model

Yantao Gong, Lei Nie, Huiling Fu, Zhenhuan He, Analysis of Passengers’ Tickets Pre-booking Behavior in High-speed Railway Based on Market Segmentation. 2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019), 2019: 194-200. DOI: https://doi.org/10.33969/EECS.V3.044.