###### Abstract  
Traditionally, research in the field of traffic safety has predominantly focused on two key areas--the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation. street view images; urban built environment; traffic black spot recognition; graph convolutional neural networks +
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0000-0001-5000-0000-00000-00000-0000-0000-0000-00000-00000-0000-00000-0000-00000-of people, vehicles, and roads becomes more prominent, which can easily produce traffic hazards and lead to traffic accidents [1; 2; 3]. Traffic black spots are defined as locations with a high incidence of traffic accidents, which account for only 0.25% of the total road network mileage, although the number of traffic accidents occurring in them accounts for 25% of the total number of accidents [4; 5; 6; 7]. Therefore, accurately identifying traffic black spots can reduce the occurrence of traffic accidents. How to accurately identify traffic black spots on the basis of existing data has not yet been uniformly determined. In previous studies, traffic accident data have mostly come from the historical accident records of the traffic management department, and the number of traffic accidents is analyzed to determine the locations of the traffic black spots by using mathematical and statistical methods [8; 9; 10]. The data are relatively singular and not easy to collect and manage, and more importantly the number of accident features is small, which does not allow for a specific analysis of the causes of traffic black spots.