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Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment

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  • Dai, Dajun

Abstract

There is a growing concern about pedestrian injuries on road. Using pedestrian crash records (2000–2007) from the Georgia Department of Transportation as a case study, this paper applied a spatiotemporal clustering technique to identify clusters of injured pedestrians and then investigated the influence of personal and environmental factors on pedestrian injuries. The Bernoulli model in SatScan was used to detect the roadway segments, where pedestrian injuries were significantly clustered. Descriptive statistics and temporal (yearly, monthly, day of week, and hourly) trends of the injuries were explored, respectively. The logistic regression model was used to assess the injury risk associated with pedestrian factors (gender, age, intoxication, and maneuvers), driver’s factors (gender, age, and intoxication), and environmental factors (light conditions, surface conditions, and weather conditions). The results showed that suburban high-activity corridors, where state highways intersect local streets, significantly elevated injury risks in crashes compared to other areas. The percentage of injuries was pronounced in summers, on the weekends, and from evenings to early mornings. Age, pedestrian maneuvers, and inadequate lighting were significant risk factors for pedestrian injuries. Walking/driving under influence and male pedestrians/motorists showed an increased risk of injuries when only the main effect of the factors was considered. The high-risk roadway segment and the risk factors highlight the need for heightened investigation and education in the specific areas and populations.

Suggested Citation

  • Dai, Dajun, 2012. "Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment," Journal of Transport Geography, Elsevier, vol. 24(C), pages 206-214.
    • Handle: RePEc:eee:jotrge:v:24:y:2012:i:c:p:206-214
    DOI: 10.1016/j.jtrangeo.2012.02.005
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    1. Bajada, Thérèse & Attard, Maria, 2021. "A typological and spatial analysis of pedestrian fatalities and injuries in Malta," Research in Transportation Economics, Elsevier, vol. 86(C).
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    3. Hei Gao & Zike Xu & Yu Chen & Yutian Lu & Jian Lin, 2022. "Walking Environment and Obesity: A Gender-Specific Association Study in Shanghai," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    4. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    5. Nadine Schuurman & Blake Byron Walker & David Swanlund & Ofer Amram & Natalie L. Yanchar, 2020. "Qualitative Field Observation of Pedestrian Injury Hotspots: A Mixed-Methods Approach for Developing Built- and Socioeconomic-Environmental Risk Signatures," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    6. Prato, Carlo G. & Kaplan, Sigal & Patrier, Alexandre & Rasmussen, Thomas K., 2019. "Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark," Journal of Transport Geography, Elsevier, vol. 74(C), pages 10-23.
    7. Young, Jason & Park, Peter Y., 2014. "Hotzone identification with GIS-based post-network screening analysis," Journal of Transport Geography, Elsevier, vol. 34(C), pages 106-120.
    8. Su, Shiliang & Zhou, Hao & Xu, Mengya & Ru, Hu & Wang, Wen & Weng, Min, 2019. "Auditing street walkability and associated social inequalities for planning implications," Journal of Transport Geography, Elsevier, vol. 74(C), pages 62-76.
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    11. Roni Utriainen & Markus Pöllänen, 2020. "Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
    12. Chen, Cong & Zhang, Su & Zhang, Guohui & Bogus, Susan M. & Valentin, Vanessa, 2014. "Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico," Journal of Transport Geography, Elsevier, vol. 38(C), pages 148-158.
    13. Jung, Soyoung & Qin, Xiao & Oh, Cheol, 2016. "Improving strategic policies for pedestrian safety enhancement using classification tree modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 53-64.
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