An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks
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, wh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2108 |
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| author | Changmin Liu Yang Wang Weikang Li Liufeng Tao Sheng Hu Mengqi Hao |
| author_facet | Changmin Liu Yang Wang Weikang Li Liufeng Tao Sheng Hu Mengqi Hao |
| author_sort | Changmin Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T00:34:19Z |
| format | Article |
| id | doaj.art-67853bc83c324a9e9f233b245d481445 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:19Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-67853bc83c324a9e9f233b245d4814452024-03-12T16:40:08ZengMDPI AGApplied Sciences2076-34172024-03-01145210810.3390/app14052108An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural NetworksChangmin Liu0Yang Wang1Weikang Li2Liufeng Tao3Sheng Hu4Mengqi Hao5School of Information Engineering, Shanxi Vocational University of Engineering Science and Technology, Jinzhong 030606, ChinaSchool of Computer Sciences, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Sciences, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Sciences, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, Beidou Research Institute, South China Normal University, Foshan 528225, ChinaSchool of Computer Sciences, China University of Geosciences, Wuhan 430074, ChinaTraditionally, 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.https://www.mdpi.com/2076-3417/14/5/2108street view imagesurban built environmenttraffic black spot recognitiongraph convolutional neural networks |
| spellingShingle | Changmin Liu Yang Wang Weikang Li Liufeng Tao Sheng Hu Mengqi Hao An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks Applied Sciences street view images urban built environment traffic black spot recognition graph convolutional neural networks |
| title | An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks |
| title_full | An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks |
| title_fullStr | An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks |
| title_full_unstemmed | An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks |
| title_short | An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks |
| title_sort | urban built environment analysis approach for street view images based on graph convolutional neural networks |
| topic | street view images urban built environment traffic black spot recognition graph convolutional neural networks |
| url | https://www.mdpi.com/2076-3417/14/5/2108 |
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