Population aging and population decline are experienced not only in South Korea but also in other countries around the world. In particular, public transportation operations, which have been centered on existing large buses, are struggling with a continuous deficit owing to the rapid population decline in rural areas, thus leading to a social issue. To address this issue, nations worldwide have attempted to find various alternatives. In South Korea, voucher taxis and city-type buses have been newly supplied in rural areas as alternatives. In this study, six city-type bus routes implemented in Yangsan-si, South Korea have been intensively reviewed in particular. The planned routes and operation status of each bus route were compared and reviewed based on geographic information systems. Six improved demand-responsive transport (DRT) operation methods were studied based on the operation patterns of city-type buses that were operated differently from the planed routes. Through this, a more suitable DRT small bus operation model for each route was proposed. Our study results will be a foundational proposal for policy makers concerned with improving public transport services and supplying new services in rural areas.

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10–20 mm/h), weak fog (<150 m), intense rain (30–40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30–40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20–30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

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.