Various studies are currently underway on PM (Particulate Matter) monitoring in view of the importance of air quality in public health management. Spatial interpolation has been used to estimate PM concentrations due to that it can overcome the shortcomings of station-based PM monitoring and provide spatially continuous information. However, PM is affected by a combination of several factors, and interpolation that only considers the spatial relationship between monitoring stations is limited in ensuring accuracy. Additionally, relatively accurate results may be obtained in the case of interpolation by using external drifts, but the methods have a disadvantage in that they require additional data and preprocessing. This study proposes a modified IDW (Inverse Distance Weighting) that allows more accurate estimations of PM based on the sole use of measurements. The proposed method improves the accuracy of the PM estimation based on weight correction according to the importance of each known point. Use of the proposed method on PM10 and PM2.5 in the Seoul-Gyeonggi region in South Korea led to an improved accuracy compared with IDW, kriging, and linear triangular interpolation. In particular, the proposed method showed relatively high accuracy compared to conventional methods in the case of a relatively large PM estimation error.

Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM_2.5 levels peaked in January, while PM₁₀ levels peaked in April. Both were at their lowest levels in July. Further, PM_2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM₁₀ was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO₂, and SO₄ had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.

With the increasing popularity of digital navigation systems and smartphones, the role of road signs during driving is gradually diminishing. However, owing to the inaccessibility of the technology to certain portions of the population, e.g., the elderly, and the risk of failure of communication networks, road signs continue to be an essential public tool to ensure driving safety. Although some research has been conducted on road sign-related topics, e.g., road sign recognition and image analysis, the comprehensive safety of road sign functions has not yet been researched. Accordingly, this study analyzed the factors of road signs affecting driving safety based on ordinal logistic regression analysis and derived their implications through a public survey. To improve driving safety, it is necessary to improve the intuitiveness of guide information (Daytime recognition (odds ratio): 1.547, Understanding of guide information: 1.222), select user’s necessary information (Sufficiency of guide information: 1.449) and provide variable information (Real-time guidance according to situation), establish a road guide system for various future mobility modes (Customized guidance for various forms of mobility: 1.112), and link and integrate with surrounding road facilities (Location/frequency adequacy: 1.128, Integrated installation of road facilities: 1.116).

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This study determined citizens’ perceptions and impact factors of illuminated road signs installed to ensure their visibility at night when the risk of traffic accidents is high. An ordered logit model was used to measure illuminated road signs’ impact on drivers’ perception based on the data from the citizens’ survey conducted by the Road Sign Center. According to the results, the internal (optical fiber) type variable had the highest impact, followed by the frequent fog variable and the complex road line variable. This study found that most citizens positively recognized road signs, preferred internal (optical fiber) types, and desired illuminated road signs that considered climate, environment, and road structure types. In Seoul, the importance and improvement of illuminated road signs at points where road structures are complex, such as city streets, were high. Additionally, the illuminated road sign recognition and road type variable were significant in Gyeonggi-do, which reflected the high number of citizens that commute to Seoul from Gyeonggi-do. Concerning local cities and counties and intercity roads highly affected by the climate, the impact was high at points with frequent fog. Fog affects the visibility distance, generates condensation on signs, and significantly degrades visibility. Therefore, an illuminated road sign installation method must be presented based on spatial analysis for regions vulnerable to climate, environment, and road location. Additionally, the road intersection point variable was significant in local cities and counties, which reflects the relatively lagged road infrastructure. Local cities and counties are financially poor and have numerous aged drivers; hence, central government support that considers these aspects is crucial.

This study introduces a method for classifying traffic flow segments on expressways to estimate impact zones in merging/diverging sections and accident-prone sites. I propose a spatiotemporal dynamic segmentation approach that enables real-time identification of traffic hazard sections, reflecting changes in traffic flow, as opposed to traditional traffic analysis based on predefined segments in a node–link network. This methodology uses high-resolution vehicle trajectory data to precisely identify unstable and low-speed traffic sections. Using the geohash algorithm, the area is hierarchically segmented based on the standard deviation of speed in general traffic flow, facilitating the identification of unstable traffic flow patterns. For eight expressway routes, traffic flow was categorized into stable or minimum-size spaces. From a total of 1207 segments, 943 unstable flow segments were identified. The impact zones of the merging and diverging sections on Expressway 50 were analyzed using the results of spatial segmentation. Furthermore, by comparing traffic data before and after accidents, I assessed the short- and long-term effects of accidents on traffic flow. The proposed methodology provides precise data essential for reducing the likelihood of traffic accidents and for predicting post-accident congestion and duration. The patterns of such accident impact zones can contribute to preventing secondary accidents by providing advance information to following vehicles through various communication methods, including those involving autonomous vehicles. This enables effective traffic management strategies and rapid responses to accidents.