How does street space influence crash frequency? An analysis using segmented street view imagery
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DOI: 10.1177/23998083221090962
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References listed on IDEAS
- Hanson, Christopher S. & Noland, Robert B. & Brown, Charles, 2013. "The severity of pedestrian crashes: an analysis using Google Street View imagery," Journal of Transport Geography, Elsevier, vol. 33(C), pages 42-53.
- Zhaoya Gong & Qiwei Ma & Changcheng Kan & Qianyun Qi, 2019. "Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
- Yuchen Li & Yichun Xie, 2018. "A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(5), pages 1313-1337, September.
- Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
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Keywords
Traffic safety; urban form; street view imagery; neural networks; cluster analysis;All these keywords.
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