Uncovering the association between traffic crashes and street-level built-environment features using street view images

Abstract

Investigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.

Keywords:

• Traffic crashes
• street view images
• streetscape features
• geographically weighted Poisson regression

Acknowledgments

We are grateful to Prof. May Yuan, Prof. Christophe Claramunt, and the anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The sample data and codes that support the findings of this study are available on ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.21384024.v1

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41971406, 42271470, 42001340]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011586]; State Key Laboratory of Geo-Information Engineering [No. SKLGIE2021-M-4-1]; and the China Scholarship Council (CSC) during a visit by Sheng Hu to National University of Singapore.

Notes on contributors

Sheng Hu

Sheng Hu is a Postdoctoral Scholar at the Beidou Research Institute, South China Normal University. He is also a Distinguished Associated Research Fellow at South China Normal University. His research interests include geospatial artificial intelligence and geospatial data science.

Hanfa Xing

Hanfa Xing is a Professor for Geoinformatics at South China Normal University. He is also an Associated Dean of the Beidou Research Institute, South China Normal University. His research interests include GIScience, spatio-temporal data mining, and LULC analysis.

Wei Luo

Wei Luo is an Assistant Professor in Geography Department at National University of Singapore, where he leads the GeoSpatialX Lab. He received the Master degree from Geography Department at University at Buffalo and PhD degree in GeoVISTA Center at the Penn State University. His main research focuses on GIScience, geovisual analytics, GeoAI, spatial epidemiology, and international trade and supply chains.

Liang Wu

Liang Wu is a Professor for Geoinformatics at School of Computer Science, China University of Geosciences. His research interests include GIScience, geospatial knowledge graphs, and machine learning in the geospatial domain.

Yongyang Xu

Yongyang Xu is an Assistant Professor at School of Computer Science, China University of Geosciences. His research interests include geospatial knowledge graphs and urban computing.

Weiming Huang

Weiming Huang received his PhD in Geographical Information Science at Lund University, Sweden in 2020. He is a Wallenberg-NTU Postdoctoral Fellow at Nanyang Technological University, Singapore. His research interests mainly include spatial data mining and geospatial knowledge graphs.

Wenkai Liu

Wenkai Liu is a Distinguished Research Fellow at South China Normal University. His research interests include spatio-temporal data mining and urban thermal environment.

Tianqi Li

Tianqi Li is currently a master student at School of Geography and Information Engineering, China University of Geosciences. Her research interests include GIScience and geospatial data science.