Disaster management teams could learn much from the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. The virus outbreak provoked public fear, which resulted in a mass reduction in transit use in the Seoul Metropolitan Area (SMA). Its effect differed among socioeconomic groups and geographical areas. A typical way to analyze this is to associate individual reactions to the disaster with socioeconomic characteristics. However, a more structured approach, which considers behavioral characteristics resulting from the societal position of an individual, would identify the basic reason for such associations. The study hypothesized that the degree of fixity that individuals have in their daily life may elucidate these associations. When fear is prevalent, people having the flexibility to change their lifestyle will make more changes in daily activities and travels. The study examined the influence of public fear of a pandemic disease on travel behavior and the effect of life fixity on individual response to the fear. To this end, smart card data of transit use and changes in travel behavior during the MERS period were examined. The study found that fear was powerful and influenced travel behavior differently depending on life fixity levels and regional characteristics.

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.

The objective of this study is to open up a new paradigm for freight demand analysis. This study proposes to use physical distribution channel choice models which can replace so called freight mode and route models. The basic notion of this approach is that shippers choose the distribution channels which minimize the overall logistics costs including inventory cost, transportation cost, etc., which are not incorporated well in the traditional four-step based approach. As a case study, this research developed physical distribution channel choice models reflecting decision making of the firms and analyzed how choice decision factors influence the selection of distribution channels. The distribution channel survey data in Korea was used to do an empirical study. In creating a choice set, Korean distribution channels were classified into two main choice channels which are direct and indirect channels. It was found that the logit model was suitable for the modeling distribution channel choice and the proposed new paradigm for freight demand analysis could be a viable alternate approach for the traditional four-step method.

Hydrogen mobility is expected to be a crucial element in decarbonizing fossil fuel-based transportation. In South Korea, hydrogen mobility has successfully formed an early market led by fuel cell passenger cars under strong support policies. Nevertheless, the fuel cell vehicle (FCV) market is still in its infancy, and current challenges must be overcome to achieve mass-market adoption. This study aims to identify the current challenges in the diffusion of FCVs in Korea. We identified the key challenges facing FCVs from a consumer perspective with data from the latest FCV customer survey. The data were applied to estimate ordered logit models of fuel cell car satisfaction and purchase intention. Significant challenges in Korea were identified from the perspective of vehicles, infrastructure, and renewable energy. Vehicle-related challenges include concerns about vehicle durability, such as recalls and repairs, and maintenance and repair costs. Infrastructure-related challenges include the fueling accessibility and fueling failures due to hydrogen refueling station facility failures, or hydrogen supply problems. Challenges related to renewable energy include the low proportion of hydrogen from renewable sources. To achieve the large-scale diffusion of FCVs, it is important to maintain support policies and attract new FCV demand, such as long-distance heavy-duty vehicles.

In the cost–benefit analysis of urban transportation investment, a logsum-based benefit calculation is widely used. However, it is rarely applied to inter-regional transportation. In this study, we applied a logsum-based approach to the calculation of benefits for high-speed projects for inter-regional railways in Korea’s long-term transportation plan. Moreover, we applied a behavioral model in which an agent travels beyond the zones assumed by an aggregate model. In the case of South Korea, such a model is important for determining transportation priorities: whether to specialize in mobility improvement by investing in a high-speed railway project, such as the 300 km/h Korea Train eXpress (KTX), or to improve existing facilities, such as by building a relatively slower railroad (150–250 km/h) to enhance existing mobility and accessibility. In this context, if a new, relatively slow railroad were constructed adjacent to a high-speed railroad, the benefits would be negligible since the reduction in travel time would not sufficiently reflect accessibility improvements. Therefore, this study proposes the use of aggregate and agent-based models to evaluate projects to improve intercity railway service and conduct a case study with the proposed new methodology. A logsum was selected to account for the benefits of passenger cars on semi-high-speed and high-speed railroads simultaneously since it has been widely used to estimate the benefits of new modes or relatively slow modes. To calculate the logsum, this study used input data from both the aggregate and individual agent-based models, and found that an analysis of the feasibility of inter-regional railroad investment was possible. Moreover, the agent-based model can also be applied to inter-regional analysis. The proposed methods are expected to enable a more comprehensive evaluation of the transport system. In the case of the agent-based model, it is suggested that further studies undertake more detailed scenario analysis and travel time estimation.

The existence of state dependence derived from panel data has played a very important role in studying employment and labor policies. This study is about state dependence of the transportation sector using retrospective panel survey data. The Ministry of Land, Infrastructure and Transport of Korea has conducted the survey to monitor changes in vehicle ownership and usage nationwide and to prepare measures when oil prices tend to rise sharply. From this data, we identify the existence of state dependence on passenger cars, public transportation, and nonmotorized modes. To do this, we estimate and analyze the dynamic random effects probit model that explains the selection of each transportation mode after controlling for the unobserved individual heterogeneity. Our results indicate that despite the rise of oil prices, behavior of habitual use (i.e., state dependence) of transportation modes is found in all three modes. The amount of state dependence of nonmotorized modes was the largest, followed by passenger cars and public transportation. From the estimated models, important policy implications can be drawn from the fact that the presence of state dependence and the importance of early habit formation are important not only in nonmotorized modes but also in public transportation. In other words, if policy makers want to encourage people to use public transportation in a new city, it suggests that a sufficient and convenient public transportation network should be built before people move to the city. Once cities are built without sufficient public transportation networks and people have become accustomed to using private cars, then it will be more difficult to change their transportation modes, requiring much more social efforts and costs.