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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To maximize its synergetic effect across the cycle from prevention to response to post-crime management, crime prevention requires a balanced combination of spatial urban design and advanced crime prevention technologies for crime prediction and real-time response. This study derived intelligent Crime Prevention Through Environmental Design (CPTED) services and suggested a decision model based on the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to implement these services in municipal control centers. The analysis results are summarized as follows. First, this study established a fuzzy TOPSIS-based decision-making support model enabling local government control centers to effectively select intelligent CPTED service elements. Second, overall, operator-led Closed-Circuit Television (CCTV) and platform control technologies were identified as significant components of intelligent CPTED service elements. Third, a comparison by city size revealed that large cities in the Seoul metropolitan area rated system services for control based on advanced crime prevention infrastructure (e.g., the crime monitoring systems and real-time control drones/robots) relatively higher. In contrast, small and medium-sized cities in other provinces rated services that were perceptible to residents and improved crime-prone environments (e.g., artificial intelligence (AI) video analysis for living safety) relatively higher.

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.