스마트모빌리티 연구실

New towns often experience a structural transit gap in early stages, where transport supply lags behind population growth. Demand-responsive transit (DRT) has emerged as a promising complementary solution; however, most studies rely on MNL-based ICLV models that do not account for error covariance across alternatives. This study applies an ICLV model, integrating an Error Component Mixed Logit kernel with latent variables, to analyze mode choice behavior in transit-poor new towns. Based on an SP-off-RP survey of 644 residents in new towns, 2576 observations were analyzed. The model incorporates five latent variables, including Transit Dissatisfaction, Convenience, Safety, Travel Time, and Travel Companion Sensitivity, and captures unobserved correlations through a two-level nesting structure. Results show that DRT has a significantly positive alternative-specific constant, indicating latent acceptance beyond observable attributes. DRT adoption is more common among transit-poor new town residents and highly educated individuals, but less common among car owners. Users are more sensitive to access and waiting time than to in-vehicle time. Convenience, Safety, and Travel Time significantly influence DRT utility, while Travel Companion Sensitivity reveals heterogeneous effects across modes. These findings provide behavioral insights for designing effective DRT strategies in transit-poor new towns.

This article investigates the demand for demand responsive transit (DRT) services in shrinking rural areas, focusing on factors influencing adoption. As rural populations decline and traditional transit options become less viable, DRT offers a flexible and cost-effective alternative. Using an integrated choice and latent variable model, this study analyzes stated preference data from a survey conducted in South Korea’s rural regions experiencing population decline. It examines socio-demographic factors, service attributes, and latent traits such as sociability, tech-savviness, and punctuality. Results show that socio-demographic characteristics, including age, income, and children’s status, significantly affect mode choice, with younger individuals and higher-income households more likely to choose DRT. The study highlights that residents of severely shrinking areas are less likely to adopt DRT, suggesting population decline affects service viability. Additionally, latent variables like sociability, tech-savviness, and punctual mindset positively influence DRT adoption. Key attributes, including in-vehicle travel time, access time, egress time, cost, and detour time, significantly determine mode choice, with longer travel times and higher costs negatively affecting DRT preference. These findings highlight the importance of ensuring service efficiency, cost-effectiveness, and accessibility for the successful implementation of DRT in rural areas. The article offers policy recommendations, including targeted service design, infrastructure improvements, and financial incentives. It also suggests future research exploring induced demand and long-term user adaptation to DRT in rural settings.

Understanding the dynamics behind inter-episode durations of discretionary activities can lay the groundwork for expanding activity-based models beyond single-day representation to encompass multi-day forecasting. Although the field has long recognized the variability in day-to-day activities, many advanced activity-based models still neglect the interdependencies among activities spanning multiple days. This paper examines the intervals between successive participations in grocery shopping, non-grocery shopping and leisure activities specifying different finite mixture models using three parameter Weibull and shifted-log-logistic baseline hazards. Besides accounting for unobserved heterogeneity in a non-parametric fashion, the models accommodate time-varying covariates, household-, individual-, and land use characteristics, and are estimated using over two years of GPS data collected in the Netherlands between 2019 and 2022. Restrictions during COVID-19 have been incorporated in the models as a time-varying covariate since the data collection period encompasses the pandemic period. The significant shape parameters for both the Weibull and log-logistic segments suggest increasing hazards with time, warranting the usage of more flexible models accommodating (non-)monotonically changing hazards.

Efforts to provide demand-responsive transit (DRT) services, a variation on traditional bus operations, are spreading to many local governments to secure the right to mobility for underserved areas, classes, and times of day. While DRT is intended to complement or replace traditional public transportation, it is crucial for local governments to set appropriate fares in order to successfully implement and sustainably provide this service. To this end, many studies have analyzed the average user's willingness to pay for new transportation services entering the market, such as DRT. However, these studies were limited in their ability to identify differences in willingness to pay based on user experience. User experience is important in the sense that it reflects the context in which a user normally uses the service. This is because users may have different preferences for the same transportation service depending on their characteristics and experiences. To this end, this study conducted a latent preference survey among respondents who had used DRT services during the pilot project to identify the preference structure of service options and estimate the willingness to pay based on user characteristics and user experience. The model was estimated using a mixed logit model that allows for differences in preferences among users. The results of the analysis clearly confirm the dominant influence of alternative characteristics on DRT preferences, while some user characteristics and experience attributes also have significant, albeit limited, effects. The results of this study have important implications for municipalities' efforts to provide new transportation services.