As China’s high-speed rail and civil aviation gradually move from a competitive relationship to a cooperative relationship, air-rail integration services (ARISs) have begun to be promoted, and an increasing number of passengers are choosing the combined travel mode of high-speed rail and aviation. As two long-distance transportation modes are involved, transfer time also affects transfer service satisfaction, and the factors affecting the transfer time of ARIS need to be further clarified. In this study, a stated preference (SP) survey on the transfer reservation time of ARIS passengers was conducted, the perception accuracy of the transfer reservation time was analyzed, and the influencing factors of the transfer reservation time in each link were analyzed by using a multivariate ordered logistic regression model. The results show that the average transfer time of air-rail combined transportation passengers is 102 to 128 min; more than half of passengers’ reservation time is reasonable; and passengers’ genders, educations, occupations, incomes, travel modes, costs, and distances have different degrees of impact on the transfer reservation time. This study holds a certain guiding significance for improving the service quality of air-rail intermodal transportation and improving transfer efficiency and provides a theoretical basis for personalized and customized services for air-rail intermodal passengers.

With the progress of regional integration in China and the growth of passenger travel demand, a single travel mode has been unable to meet passengers’ cross-regional, multimodal, and personalized travel needs. Air-rail integration service (ARIS) is gradually catching on with travellers. Compared with developed countries, ARIS remains in its infancy in China, with a low penetration rate. Against this backdrop, this study presents an SP survey of ARIS passengers at Shijiazhuang Zhengding International Airport as an example and analyses the factors affecting their transfer process using multiple ordinal logistic regression. The satisfaction analysis results help to construct a simulation model of ARIS of Shijiazhuang Zhengding International Airport by using AnyLogic to verify the optimization effect. The research results show a significant impact on the ARIS transfer process by gender, education, occupation, travel mode, travel cost, location of the shuttle bus, arrival frequency of shuttle bus, manual baggage check-in service, self-service check-in, and the performance of security check equipment. The adoption of optimization measures will reduce ARIS shuttle bus, baggage check-in, and security check-in times by 68%, 50%, and 45%, respectively.

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT.