Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO₂ emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT.

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

Based on the theory of planned behavior, this study identifies the causal relationship between attitudinal factors and intention to use transportation mode. A structural equation model was developed based on twelve hypotheses. The main findings and implications of this study are as follows. First, people who want to express themselves through cars have a high intention to use personal vehicles, and they purchase cars for this purpose. If the shared vehicle service provides a vehicle rental that reflects individual tastes, those who want to own the vehicle will use the shared vehicle. This could be a solution to the parking problem. Second, those who perceive travel as a disutility have a low intention to use public transportation. If fare discounts are applied when transferring public transportation and micro-mobility, it is expected that the use of public transportation will increase due to reduction of access time for public transportation. Third, people who like to drive have a high intention to use personal vehicles and micro-mobility. Providing space for driving cars as a leisure activity may be one of the ways to prevent traffic accidents that may occur in the future due to a mixed flow of autonomous vehicles and conventional vehicles.

This study aims to identify the causal relationship between travel and activity times using the dataset collected from the 2019 Time Use Survey in Korea. As a statistical solution, a structural equation model (SEM) was developed. A total number of 31,177 and 20,817 cases were used in estimating the weekday and weekend models, respectively. Three types of activities (subsistence, maintenance, and leisure), 13 socio-demographic variables, and a newly proposed latent variable (vitality) were incorporated in the final model. Results showed that (1) the magnitude of indirect effects were mostly greater than that of direct effects, (2) all types of activities affected travel time regardless of what the travel purpose was, (3) travel can be treated as both a utility and disutility, and (4) personal status could affect the travel time ratio. It indicates the significance of indirect effects on travel time, thereby suggesting a broad perspective of activities when establishing a transportation policy in practical areas. It also implies that unobserved latent elements could play a meaningful role in identifying travel time-related characteristics. Lastly, we believe that this study contributes to literature by clarifying a new perspective on the lively debated issue discussing whether travel time is wasted or productive.

This study proposes the Random Combined Utility Maximization (RCM) model, a novel utility framework for transportation choice that integrates independent utility with relative utility comparisons on a consistent scale. The RCM model retains the behavioral foundation of random utility maximization while explicitly representing comparison-based decision making across alternatives. The relative utility incorporates the Irrelevance of Statewise Dominated Alternatives (ISDA), implying that comparisons become behaviorally negligible when utility differences are extreme. To operationalize ISDA in a smooth and estimable form, we apply a hyperbolic secant transformation, such that relative utility is primarily generated among alternatives with similar utility levels. This formulation provides a structural refinement that addresses limitations of existing utility specifications without introducing additional explanatory variables. Using multiple transportation choice datasets and a common specification based on time and cost, we benchmark the RCM model against widely used alternatives, including Random Utility Maximization, Random Regret Minimization, and Relative Advantage Maximization. Across all datasets considered, the RCM model delivers the best overall performance in terms of model fit and predictive accuracy. Although the magnitude of improvement varies across datasets, the consistent gains across evaluation criteria indicate that the RCM model captures observed choice behavior more reliably than conventional utility formulations.