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Exploring fatal/severe pedestrian injury crash frequency at school zone crash hotspots: using interpretable machine learning to assess the micro-level street environment

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  • Zhang, Kaihan
  • Tamakloe, Reuben
  • Cao, Mengqiu
  • Kim, Inhi

Abstract

Several countries have implemented designated school zones and installed traffic calming measures to enhance the safety of vulnerable pedestrians near schools. While macro-level built environment attributes (e.g., land use) have been widely acknowledged in relation to the role they play in urban traffic safety, the effects of micro-level streetscape characteristics on crash frequency have not been investigated to any significant extent. Moreover, the associations between these environmental features and crashes in school zones remains largely unknown. To address this issue, we first identified school zone-related crash hotspot using spatiotemporal hotspot mining on a comprehensive dataset of 20,484 pedestrian-vehicle crashes between 2017 and 2021 in Seoul, South Korea. Streetscape characteristics were analysed using street view imagery and advanced computer vision techniques to extract and classify pixel-wise visual elements. Preliminary findings reveal spatiotemporal variations in fatal and severe injury (FSI) crashes, with school zones in central commercial and industrial areas emerging as persistent crash hotspots that have remained statistically significant hotspots for 90 % of the study period. Further impact analysis using interpretable machine learning helped to uncover the non-linear relationships between both micro and macro environmental features and FSI frequency. Lower levels of street enclosure and walkability were associated with a higher frequency of FSI crashes, while increased openness and imageability were also correlated with more FSI incidents. Additionally, street greenery was found to reduce FSI crashes once it reached a certain threshold. Our findings extend existing knowledge of how the built environment and streetscape design influence pedestrian safety in school zones, paving the way for more targeted interventions to plan safer pedestrian environments around schools.

Suggested Citation

  • Zhang, Kaihan & Tamakloe, Reuben & Cao, Mengqiu & Kim, Inhi, 2024. "Exploring fatal/severe pedestrian injury crash frequency at school zone crash hotspots: using interpretable machine learning to assess the micro-level street environment," Journal of Transport Geography, Elsevier, vol. 121(C).
    • Handle: RePEc:eee:jotrge:v:121:y:2024:i:c:s0966692324002436
    DOI: 10.1016/j.jtrangeo.2024.104034
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    References listed on IDEAS

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    1. Shaohua Wang & Yanyan Chen & Jianling Huang & Ning Chen & Yao Lu, 2019. "Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, May.
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    3. Yang, Linchuan & Ao, Yibin & Ke, Jintao & Lu, Yi & Liang, Yuan, 2021. "To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults," Journal of Transport Geography, Elsevier, vol. 94(C).
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    5. Behram Wali & Asad Khattak, 2020. "Harnessing Ambient Sensing & Naturalistic Driving Systems to Understand Links Between Driving Volatility and Crash Propensity in School Zones: A generalized hierarchical mixed logit framework," Papers 2010.12017, arXiv.org.
    6. Wu, Fangning & Li, Wenjing & Qiu, Waishan, 2023. "Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques," Journal of Transport Geography, Elsevier, vol. 113(C).
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