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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.

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.

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.