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.

Research related to thermal cameras, which are major control measures, is increasing to overcome the limitations of closed-circuit television (CCTV) images. Thermal cameras have the advantage of easily detecting objects at night and of being able to identify initial signs of dangerous situations owing to changes in temperature. However, research on thermal cameras from a comprehensive perspective for practical urban control is insufficient. Accordingly, this study presents a thermal camera-based abnormal-situation detection service that can supplement/replace CCTV image analysis and evaluate service preferences. We suggested an integrated Fuzzy AHP/TOPSIS model, which induces a more reasonable selection to support the decision-making of the demand for introducing thermography cameras. We found that developers highly evaluated services that can identify early signs of dangerous situations by detecting temperature changes in heat, which is the core principle of thermography cameras (e.g., pre-fire phenomenon), while local governments highly evaluated control services related to citizen safety (e.g., pedestrian detection at night). Clearly, while selecting an effective service model, the opinions of experts with a high understanding of the technology itself and operators who actually manage ser-vices should be appropriately reflected. This study contributes to the literature and provides the basic foundation for the development of services utilizing thermography cameras by presenting a thermography camera-based abnormal situation detection service and selection methods and joint decision-making engagement between developers and operators.

Despite the high public interest in particulate matter (PM), a key determinant for indoor and outdoor activities, the current PM information provided by monitoring stations (e.g., data per administrative district) is insufficient. This study employed the closed-circuit television (CCTV) cameras densely installed within a city to explore the spatial expansion of PM information. It conducted a comparative analysis of PM estimation effects under diverse experimental conditions based on AI image recognition. It also fills a gap by providing an optimal analysis framework that comprehensively considers the combination of variables, including the sun’s position, day and night settings, and the PM distribution per class. In the deep learning model structure and process comparison experiment, the hybrid DL-ML model using ResNet152 and XGBoost showed the highest predictive power. The classification model was better than the ResNet regression model, and the hybrid DL-ML model with the post-processed XGBoost was better than the single ResNet152 model regarding AI prediction of PM. All four experiments that excluded the nighttime, added the solar incidence angle variable, applied the distribution of PM per class, and removed the outlier removal algorithm showed high predictive power. In particular, the final experiment that satisfied all conditions, including the exclusion of nighttime, addition of solar incidence angle variable, and application of outlier removal algorithm, derived predictive values that are expected to be commercialized.

To ensure the structural stability of road signs against strong gusts of wind, perforated road signs with sign boards perforated at regular intervals have been adopted for use in urban road infrastructure. However, ASCE 7-22 standards do not consider the drag coefficient of signs with low porosity, i.e., less than 30%. Therefore, here, computational fluid dynamics (CFD) analysis was performed to analyze the drag coefficient of perforated road signs with a porosity of less than 30%. The aspect and clearance ratios, which are the width/height ratio of a perforated road sign and the height of the perforated road sign/its distance from the ground, respectively, were set as parameters. Subsequently, the drag coefficient of perforated road signs with a porosity of 30% was calculated, and the effect of porosity and wind speed on the drag coefficient of perforated road signs was analyzed. Results show that the drag coefficient of perforated road signs decreased as the porosity increased, with a drag coefficient reduction effect of up to 0.85 times. Furthermore, perforated road signs with a high clearance ratio exhibited significant decreased drag coefficient. For perforated road signs, the drag coefficient does not show a clear correlation with wind speed.

The growing frequency of extreme weather, earthquakes, fires, and environmental hazards underscores the need for real-time monitoring and predictive management at the urban scale. Conventional three-dimensional spatial information systems, which rely on orthophotos and ground surveys, often suffer from computational inefficiency and data overload when processing large and heterogeneous datasets. To address these limitations, this study introduces a three-dimensional GeoHash-based geocoding algorithm designed for lightweight, real-time, and attribute-driven digital twin operations. The proposed method comprises five integrated steps: generation of 3D GeoHash grids using longitude, latitude, and altitude coordinates; integration with GIS-based urban 3D models; level optimization using the Shape Overlap Ratio (SOR) with a threshold of 0.90; representative object labeling through weighted volume ratios; and altitude correction using DEM interpolation. Validation using a testbed in Sillim-dong, Seoul (10.19 km²), demonstrated that the framework achieved approximately 9.8 times faster 3D modeling performance than conventional orthophoto-based methods, while maintaining complete object recognition accuracy. The results confirm that the 3D GeoHash framework provides a unified spatial key structure that enhances data interoperability across querying, visualization, and simulation. This approach offers a practical foundation for operational digital twins, supporting high-efficiency 3D mapping and predictive disaster management toward resilient and data-driven urban systems.

The utilization of various UAVs such as facility inspection and delivery is increasing. Ensuring safety in carrying out various UAV missions is an important issue related to human and property damage. Safe operation of UAVs flying autonomously along pre-located routes requires safe route planning to prevent collisions between aircraft in consideration of ground and air obstacles. Pass flight is also essential to minimize damage in the event of a crash. This paper deals with the optimal UAV 2D routing plan considering ground compatibility for flight safety. High ground compatibility here means that the damage caused by the crash is less than when it is not. In this paper, we first defined dynamic and static obstacles. Next, we derived ground compatibility considering dynamic and static obstacles. We used estimation algorithms and land use data to calculate ground compatibility values for flight safety, and through experiments, we calculated an optimal 2DUAV route plan for flight safety.