How do differences in airline passengers’ satisfaction with connectivity modes affect last-mile travel choices? A SALC modeling based on RRM

The rapid growth of cars has brought a lot of parking and road traffic problems around large airports. Parking facilities around the airport can be used to reduce the problem of excessive concentration of parking demand. Based on a survey conducted at the T3 terminal of Beijing Capital International Airport, this paper established a nested logit model for parking location and connection mode choice for long-term parking travelers. The research results indicate that the best location of off-site parking facility layout is within 5 km from the airport. When the distance between the parking facility to the airport is more than 10 km, the recommended connection mode is free airport rapid rail transit. Long-term parking travelers who are within eight districts, with no companions, long driving time and self-paid for parking fee are more willing to choose off-site airport parking.

The aviation sector faces increasing pressure to address climate change as its contribution to global CO2 emissions continues to rise. This study investigates how passengers’ awareness of environmental issues and perceptions of sustainable airline practices affect their Green Air Travel Behavior (GTB). Drawing upon the Theory of Planned Behavior (TPB) and extending it with constructs such as Environmental Awareness (EA), Perceived Service Quality (PSQ), and Green Trust (GT), the research examines their impact on GTB. Using a quantitative design, data were collected from 300 airline passengers and analyzed with Structural Equation Modeling (SEM). Results reveal that EA strongly influences PSQ, GT, Attitude (ATT), and Intention (ITN), highlighting its role as a key antecedent. PSQ significantly enhances GT, while both GT and ATT directly predict GTB. However, the effect of ITN on GTB was not significant, indicating an intention–behavior gap. The findings underscore the importance of awareness, trust, and service quality in promoting sustainable air travel, while also pointing to barriers that hinder intentions from becoming actions. Theoretically, the study extends TPB within green aviation, and practically, it provides guidance for airlines and policymakers seeking to advance SDG 13: Climate Action through sustainable air travel strategies.

The “last-mile problem” of public transportation is one of the main obstacles affecting travelers who choose to utilize public transport. Although autonomous vehicles (AVs) have made much progress, they have not been officially put into commercial use. This paper adopts stated preference experiments to explore the impact of shared AVs on the last-mile travel behavior of metro users and takes Wuhan as an example for case analysis. First of all, this paper establishes a structural equation model (SEM) based on the theory of planned behavior to explore latent psychological variables, including travelers’ attitudes (ATTs), subjective norms (SNs), perceived behavior control (PBC), and behavioral intention of use (BIU) toward AVs. These latent psychological variables are incorporated into the latent class (LC) logit model to establish a hybrid model with which to study the factors and degree of influence on the travel mode choices of travelers for the last mile of their metro trips. The results show that travelers have preference heterogeneity for the travel mode choices for the last mile of metro trips. Through the analysis of LCs, education, career, and income significantly impact the classification of LCs. The latent psychological variables towards AVs have a significant impact on the travel behavior of respondents, but the impacts vary among different segments. Elastic analysis results illustrate that a 1% increase in the travel cost for shared AVs in segment 1 leads to a 7.598% decrease in the choice probability of using a shared AV. Respondents from different segments vary significantly in their willingness to pay for their usage, and the value of travel time for high-income groups is relatively higher.