An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

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.

How Does the Built Environment Affect Mechanical Parking Space Planning: A Case Study in Xi’an City

“…Traditional methods such as Ordinary Least Squares (OLSs) [30], the logit method [1] h, and Geographical Weighted Regression (GWR) [31] have been used to dig out the relationship between parking-related variables and built environment indicator systems. Machine learning, which has higher predictive performance, has been widely used in the transportation field [21,[32][33][34]. Due to higher predictive accuracy and operational efficiency, lots of researchers have used the Gradient Boosting Decision Tree (GBDT) model to analyze the influence of the built environment on travel-related variables [33,35,36], or to predict parking-related variables [21,37].…”

“…The whole modeling process requires that the spatial analysis unit should not be too large in order to ensure the validity of the measurement of the built environment on the one hand and to highlight the intrinsic variability of the factors of the built environment on the other hand. In reference to the selection of buffer zones in the study on the built environment and motor vehicle travel behavior [34], this paper finally selects the 500 m buffer zone of the parking lots as the research unit. Ten indicators were selected from the "7D" system to construct the independent variable system.…”