###### Abstract
The emergence of geographic big data and the rapid development of machine learning and deep learning make it possible to portray the built environment of urban roads from street view image data [11; 12; 13]. In real life, traffic accidents are also more likely to occur on densely populated streets with crowded and dilapidated buildings, without better road infrastructure, and with missing road markings and blurred and uneven road surfaces [14; 15; 16]. These features are all part of the built environment of urban roads, which can be described by street image data. Using the existing deep learning semantic segmentation technology, street image data can be segmented into more than 100 urban built environment features [13], which could be very useful as a supplement to a traffic management department's historical accident record data feature factors, so as to carry out a specific analysis of the formation of the traffic black spots. Traffic black spot identification is the focus of research in the field of traffic safety. By accurately identifying traffic black spots in urban roads, urban traffic management departments can make targeted improvements to reduce the incidence of traffic accidents to protect people's right to life, property, and health. In this paper, on the basis of the above research, street view image data are assessed using deep learning semantic segmentation technology to describe the characteristics of the built environment of urban roads, so as to obtain the information containing the characteristics of the built environment of urban roads, which can be used to identify black spots in urban road traffic. At the same time, the data from traffic accidents and street view image data are processed and constructed into the structure of a graph network and a graph convolutional neural network (GCNN) model is used to identify black spots in traffic. This GCNN model is used for the recognition of traffic black spots to make the recognition results more accurate. Firstly, taking the road intersection as the research unit, the street view image data are extracted using a semantic segmentation model to portray the built environment of urban road intersections and machine learning models, such as support vector machine (SVM), gradient boosting (GBDT), and random forest (RF) models, which are used to identify the traffic black spots. Then, the traffic black spot recognition method is improved by considering that the interconnection of built environment features of adjacent road intersections will have an impact on the formation of traffic black spots. The traffic accident data and street scene image data are processed, the graph network structure is constructed, and the graph convolutional neural network (GCNN) model is used for the recognition of traffic black spots. In the analysis of the causes of traffic black spots, the importance of each feature of the built environment of urban road intersections on the formation of traffic black spots and the interconnections between each feature are analyzed to make the research results more reliable. The proposed method can effectively improve the urban road planning and design process and make human environment planning and design more scientific, more reasonable, and more in line with the standard of the ideal modern city. In addition, the street scene image data are easier to collect and manage, which can save a significant amount of manpower cost and time cost. In this paper, we take the Kowloon Peninsula of Hong Kong as the study area and road intersections as the study unit, and traffic black spot identification and cause analysis are the research objectives. The built environment feature dataset of urban road intersections is constructed from street view images by the semantic segmentation model, which is used to identify and analyze the causes of traffic black spots at urban road intersections. The main contributions of this study are as follows:  
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