Research On Passenger Choice Behavior And Market Segmentation Of High-speed Railway Based On Latent Class Analysis

The large-scale operation of high-speed railway makes more and more people choose high-speed trains as initial transport. However, some serving problems still remain unresolved, such as unstable demand and supply balance, as well as unreasonable ticket price. Meanwhile, fierce competition from airline and highway makes the situation worse. So, high-speed railway revenue management becomes the focus of researchers, with the government now allowing the ticket price formulation to be made by market, and research on passenger choice behavior and market segmentation are fundamental researches of revenue management. Large amounts of data about passenger travel behaviors are accumulated since the birthday of real-name ticketing system. These data are not well used for passenger organization and marketing, let alone raising ticket income directly. Thus, high-speed railway ticket data is used to analysis passenger travel characteristics and to focus on study about passenger choice behavior and market segmentation, to give suggestions for high-speed railway revenue management.Firstly, an analysis of passenger travel characteristics and passenger choice behaviors is made in the study. It includes a complete conclusion of impacts on passenger travel characteristics from different factors, which are passenger attributes, product attributes and purchasing behavior. Different detail data, such as departure day of week (DOW), departure time of day (TOD), trip distance, stopping pattern, ticket purchasing mode, ticket purchasing time and advance booking days are considered and used to explain the passenger flow characteristics. Besides, the cross-distribution and impacts between each two of passenger attributes, product attributes and purchasing behavior separately are also explained in detail. A statistical correlation result is presented in the end.Secondly, suitable elements are choosed to create a comprehensive model in this study based on latent class model. It presents a latent class analysis and it leads to a detail passenger submarket. Both Beijing-Shanghai high-speed railway and Shanghai-Nanjing high-speed railway are presented as two examples to prove the thesis. The fruitful result states when dividing passengers market into three categories of Beijing-Shanghai high-speed railway and five categories of Shanghai-Nanjing high-speed railway, the matching rates would be about 93% and 84%. New passenger category names are also created based on this result.Finally, five ticket price strategies of high-speed railway are stated in the latter part of this study, as well as discounting suggestions. Suggestions for Beijing-Shanghai high-speed railway and Shanghai-Nanjing high-speed railway are gived separately, which contributing to high-speed railway industry with guiding meaning.