Analysis of Travel Mode Choice Behavior between High-Speed Rail and Air Transport Utilizing Large-Scale Ticketing Data

Abstract

As essential infrastructure, high-speed rail (HSR) and air transport (AT) play crucial roles in socioeconomic development. With their continuous expansion in China, the overlap of HSR and AT networks has increased, providing travelers with more choices for intercity travel. Because fierce competition in the medium-to-long-distance segment affects the market share and transport capacity dispatching, the travel choice between HSR and AT has been of intense interest. This study utilized a unique fusion dataset collected from two separate organizations to conduct an empirical analysis of the travel mode choice behaviors of individuals when choosing between HSR and AT. A multinomial logit (MNL) model was adopted to examine the influences of key factors on passenger choice preferences. The results showed that the fitting effect of the MNL model was satisfactory, and the parameters were strongly interpretable. The McFadden Pseudo R2 with a city-pair fixed effect in the MNL model increased by 17.3% compared with that without the city-pair fixed effect. All the related explanatory variables, including the trip distance by high-speed train, demography, ticket purchasing, and travel behavior characteristics, had significant positive effects on the passengers’ choice of AT, with trip distance having the largest effect. According to the parameter estimation, 1,160 km was the division for individual choice between HSR and AT. This study also compared the prediction accuracies of the MNL model and eight classical machine-learning models and found that random forest had the best performance. This study provides a new framework for analyzing travel choice modeling when choosing between HSR and AT.