No abstract

High-speed metropolitan railway services have been introduced in several mega cities to solve traffic problems stemming from urban sprawl. The success of such huge public transportation projects depends on how to design them more attractively based on profound insight into people’s preferences in the context of travel mode choice. Although many previous studies have investigated factors affecting the preferences for high-speed metropolitan railway services in general, possible heterogeneity in the preferences by different travel contexts tends to be largely ignored. In addition, the effects of personal latent attitudes toward travel mode choice have not been investigated thoroughly in the context of using high-speed metropolitan railway services. Incorporating such latent attitudes in mode choice analysis is helpful for better understanding the motivation of choosing a travel mode. The present study investigates people’s preferences for high-speed metropolitan railway service and their heterogeneity by different travel contexts using stated choice data. In addition, the hybrid choice modeling approach is employed to identify simultaneously the relevant latent attitudes and their effects on the preference for high-speed metropolitan railway service. The data collection was conducted in 2021, with respect to the great train express (GTX) project of the Seoul metropolitan area. The estimation results suggest that the latent attitudes about risk-minimizing and comfort-seeking have different effects on people’s intention to use GTX according to travel contexts. Moreover, the difference in sensitivity to the attributes of GTX exists depending on the travel contexts.

The sprawl of megacities is increasing the need to improve public transit services to cover longer distances in shorter times than current services. High-speed metropolitan rail services have been introduced to satisfy the needs of passengers and are expected to alleviate the transportation problems caused by urban sprawl. However, the effectiveness of public transit demand inducement policies, which have been employed in various ways in South Korea, has been questioned. Thus, agencies responsible for high-speed metropolitan rail services should also evaluate whether such policies will work properly. The achievement of the policy can be evaluated as sufficiently inducing the demand of the target group, and investigated through the residence information and the current travel behavior. This study investigates the current travel behavior of users to be replaced when a new high-speed metropolitan rail service is introduced and directly analyzes the travel mode shifting effect. While previous studies focused on increasing the applicability to general services by classifying groups based on personal attributes, this study focuses on evaluating detailed policy achievements for specific services. We apply a latent class modeling approach using stated preference survey data for group classification. In particular, by employing the user’s current travel behavior as a class membership attribute, the characteristics of potential passengers of the high-speed metropolitan rail service are analyzed. The estimation results suggest that current travel behavior significantly classifies high-speed rail passengers. In addition, the policy achievement was investigated in detail by region and operation time, and several additional policy directions are presented to satisfy the policy goals.

Automated driving technologies have advanced remarkably and are expected to be a part of our lives soon. Because automated driving technology does not require a driver, a significant change in future mobility services is expected. Automated driving technology is closely related to the development of public transit services as it can significantly reduce driver labor costs and provide a more comfortable in-vehicle environment. In particular, the preference for automated mobility-on-demand services that can respond in real time to the dynamic demand through automated driving technology is growing. Previous studies have compared passengers’ preferences for automated mobility-on-demand services and other transportation modes and proposed a way to enable more passengers to use automated mobility-on-demand services. However, as the number of pilot operations increases, future research will focus on ways to improve competitiveness among automated mobility-on-demand services. This study conducts a passenger preference survey based on the characteristics of automated mobility-on-demand services. In particular, changes in the in-vehicle environment and seat selection system, which differ from existing mobility-on-demand services due to automated driving technology, are investigated. The latent class modeling approach is used to classify passengers based on stated preference data collected from the survey. The estimation results show that vehicle type and seat choice system have a significant impact on passengers’ preference for automated mobility-on-demand services. In addition, considering that a high percentage of passengers do not prefer to improve autonomy in seat reservation and the in-vehicle environment, this study suggests that cost-consuming service improvement strategies are not always appropriate.

As individuals have different thoughts about automated driving (AD) vehicles, the gap between their manual driving (MD) styles and their expected AD styles would affect their willingness to ride AD vehicles. The objective of this study is to investigate the effects of personal driving styles on AD preference and the variations of these effects by travel distance. To identify latent driving styles and their effects simultaneously, we developed a hybrid choice model using sequential stated-choice data from an online survey administered to 511 respondents in South Korea in 2019. The latent driving styles were classified as impatient, over-nervous, and cautious driving styles. The results significantly show that individuals of each style prefer AD vehicles in different situations. Impatient drivers, who are concerned about travel delays, rarely worry about the driving mode if it drives them faster. Over-nervous drivers, who lack confidence in their driving skills, tend to trust AD technology, regardless of the travel distance. Cautious drivers, who are mindful of their surroundings, prefer AD when traveling relatively short distances. The elasticities of the travel time and cost show that the driving styles have impacts on the AD preference. Moreover, the results of value of time (VoT) for AD have been estimated to differ by travel distance. The results of this study have identified the effects of personal driving styles on AD preference, providing valuable insights that can be used to improve AD vehicle related policies and marketing strategies (such as implementing price discrimination by travel distance and lowering the overall price of AD vehicles), which may increase the market share of AD vehicles.