## 6 Discussion  
In this paper, based on the street view image data, we use the semantic segmentation model to obtain the built environment features of urban roads, take the road intersection as the research unit, carry out the research idea of traffic black spot identification and its cause analysis, construct a model for identifying traffic black spots based on the built environment of adjacent road intersections, and broaden the research idea of traffic black spot identification. The research results of this paper are summarized as follows: (1) In this paper, the Tencent Street View platform and Python crawler technology are used to obtain the street view image data of Hong Kong's Kowloon area, and deep learning semantic segmentation technology is used to construct the built environment features of urban road intersections and define the concept of traffic black spots. (2) In this paper, based on the set of built environment features of road intersections, the support vector machine model, gradient boosting model, random forest model, and fully connected neural network model are applied to identify traffic black spots at road intersections, and the experimental results show the feasibility of identifying traffic black spots at road intersections based on the street view image data, and that their experimental accuracy can reach 87%. (3) In this paper, based on the above experiments, considering that the interconnection between the built environment features of adjacent road intersections will have an impact on the identification of traffic black spots, the traffic accident data and street view image data are processed, and the graph convolutional neural network model is used to identify traffic black spots at intersections and achieves better experimental performance. (4) This paper uses principal component analysis to analyze in detail several features that have the greatest impact on the formation of traffic black spots at urban road intersections, such as buildings, billboards, sky, greenery, etc., so that the traffic management department and the urban planning department can regulate the density of building development, pedestrian and vehicular flow, billboards, and greenery according to these findings.  
We acknowledge three major limitations of this work and point out three directions for future work. (1) The scope of this paper is limited to the study of only one region, Kowloon Peninsula, Hong Kong, and only road intersections were selected as the study unit, resulting in a small amount of data. In the future, we will continue to expand the size of the whole dataset so that the results will be more reliable and convincing. (2) Secondly, this paper did not consider the time division in the process of identifying traffic black spots. Since most people have different travelling purposes on weekdays and weekends, and the situation of the morning and evening rush hours on weekdays is very different from other times of the day when people and traffic gather, all these factors may affect the distribution of traffic black spots; therefore, in the next stage of this study, the identification of traffic  
Figure 28: Visualization of the results of GCNN model to identify traffic black spots.  
black spots by time period can be more refined to propose corresponding preventive measures to achieve \"one point one policy\". (3) This paper is not comprehensive enough in the research and analysis of data. In the process of data collection, the current traffic management department is not able to collect all the information of the accident correctly, there is a part of the data which cannot be recorded by the precise geographic coordinates, and the attributes of the existing data on traffic accidents are not perfect, such as the lack of records on the characteristics of the vehicles and people involved, which will have an impact on the analysis of the causes of the traffic black spots.