Many studies have been conducted to investigate the diverse human-related factors that contribute to traffic crashes. Human factors have a greater impact on crashes caused by taxi drivers with long driving distances and hours. However, due to issues related to the protection of individual data and the complexity of collecting and processing data, there are limitations in clearly identifying risk factors related to driver characteristics. In this study, we combined in-depth survey data that included characteristics of taxi drivers and the companies they belong to and taxi crash data (2017–2019) in South Korea. However, the combined data showed a high correlation or causality between variables, leading to potential problems, i.e., multicollinearity, hierarchical structure of data, and inefficient analysis. To address this issue, we applied Principal Component Analysis (PCA) to reduce the dimensionality of variables and mitigate the problem. Furthermore, we constructed a hierarchical model considering the hierarchical structure of data in corporate taxis, where drivers are affiliated with specific companies. The analysis revealed that managing fatigue at the company level, managing drivers’ diseases, and other intrinsic factors had a significant influence on Fatal-Injury (FI) crashes. These results indicate that taxi crashes are influenced significantly by both company management factors and driver-related factors. Therefore, policymakers can provide customized preventive measures that consider both aspects.

Bicycles are an eco-friendly mode of transportation, and in the capital city of South Korea, Seoul, efforts are being made to encourage citizens to use bicycles. However, without appropriate safety measures, these efforts can lead to an increase in bicycle-related traffic accidents. To promote bicycle usage while ensuring safety, this study identified various factors that influence bicycle accidents. Data were utilized that had not been properly considered in previous bicycle accident-related studies, including slope and the level of public transportation services. By considering the factors influencing bicycle traffic accidents, various models were constructed, and through comparisons of statistical indicators, the optimal model was selected geographically weighted negative binomial regression. Ultimately, three significant conclusions to ensure bicycle safety were drawn. First, across all areas of Seoul, an increase in road slope leads to a decrease in bicycle-related accidents. Furthermore, for certain Traffic Analysis Zones (TAZs), as the number of local buses (or neighborhood/community buses) increases, the bicycle traffic volume decreases, resulting in a reduction in bicycle accidents. Lastly, for some TAZs, an increase in bicycle lanes to be installed into the roadway was associated with an increase in bicycle accidents.

With growing social concern on pedestrian accidents involving children, the Korean government announced a plan to decrease the number of child deaths due to traffic accidents by 2026. Therefore, policymakers should consider various measures for school zones because a safe school walkway is essential for preventing traffic accidents around schools. Some parts of the roads within a radius of 300 m from elementary school and kindergarten entrances are designated as school zones. Certain roads experience frequent accidents within the school zone, while others experience frequent accidents outside the school zone. Hence, this study aimed to provide school zone types in Seoul by noting different occurrence accidents within and outside each school zone and suggest proper countermeasure by type. After selecting a 300 m radius analysis unit from the school zones, a distinction was made between the school zones and outside for each analysis unit. After verifying the spatial autocorrelation in each unit, hotspot analysis identified four types based on the presence or absence of hotspots in each unit. Types were defined as follows: Type A—no hotspots in school zones or outside the school zones; Type B—hotspots only outside the school zones; Type C—hotspots only the school zones; and Type D—hotspots both in school zones and outside the school zones. Subsequently, a case study was conducted to validate the types. For Types B and C, the results revealed differences in the installation of traffic safety facilities and the environment between within and outside the school zones. Therefore, Type B requires improving safety outside the school zones by expanding school zones to match the safety level within. For Type C, it implies the need to strengthen safety measures in the school zones. Lastly, for Type D, improvement projects for a safe walking environment should be implemented in primarily by conducting separate inspections.

Planning agencies need to identify where detailed pedestrian assessment is most useful before commissioning field surveys. Public spatial data can support this task, but the locations selected by a composite screening score may depend on expert judgments and indicator assumptions. This study develops a workflow for preliminary walkability screening and evaluates the stability of its spatial selections in two railway-station areas in Suwon, South Korea. Publicly available indicators are combined with expert-derived weights on a 100 m grid, and low-score locations are examined using spatial clustering, expert resampling, and alternative indicator specifications. Station-area rankings and cell-level selections responded differently to changes in expert weights and indicator specifications. A combined persistence criterion identifies 17 candidate cells in Gwanggyo Jungang and three in Suwon under Benjamini–Hochberg adjustment; the more conservative Benjamini–Yekutieli procedure reduces these counts to 12 and zero. These cells meet the specified screening criteria, but their pedestrian conditions have not been independently verified. The workflow supports initial assessment without new site observations and identifies both locations and components for subsequent investigation. Its practical contribution is to make the uncertainty of spatial selection explicit while retaining component-specific concerns that a composite criterion may overlook.