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Improving strategic policies for pedestrian safety enhancement using classification tree modeling

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  • Jung, Soyoung
  • Qin, Xiao
  • Oh, Cheol

Abstract

Pedestrian safety enhancement is a key component in reducing traffic fatalities in the Republic of Korea. The purpose of this study was to review, validate, specify, and prioritize Korea’s strategic policies for pedestrian safety enhancement using the classification tree method to model pedestrian injury severities. The findings show that pedestrian age and movement type are the two primary variables contributing to pedestrian fatalities and severe injuries. Traffic operation, road class, crash location, driver violation, and at-fault vehicle type are all secondary variables associated with pedestrian fatalities and severe injuries. Factors that contributed to crashes were compared with strategic polices for senior zones and school zones, road safety facilities, safe walking environments, and legal obligations of the driver in order to understand why certain polices are ineffective versus effective. Consequently, this study provides prescriptive analysis and specific insights pertaining to strategic policies for pedestrian safety enhancements, which can be employed in other countries for the similar purpose. For further research, this study suggests combining several other data-mining techniques with nationwide data collection.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:85:y:2016:i:c:p:53-64
    DOI: 10.1016/j.tra.2016.01.002
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    References listed on IDEAS

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    1. 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.
    2. Chen, Li & Chen, Cynthia & Ewing, Reid, 2014. "The relative effectiveness of signal related pedestrian countermeasures at urban intersections—Lessons from a New York City case study," Transport Policy, Elsevier, vol. 32(C), pages 69-78.
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    Cited by:

    1. Maria Rella Riccardi & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    2. Nikolaou, Paraskevas & Dimitriou, Loukas, 2018. "Evaluation of road safety policies performance across Europe: Results from benchmark analysis for a decade," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 232-246.
    3. Roeger, Alexandra & Tavares, António F., 2018. "Water safety plans by utilities: A review of research on implementation," Utilities Policy, Elsevier, vol. 53(C), pages 15-24.
    4. Haque, M. Ohidul & Haque, Tariq H., 2018. "Evaluating the effects of the road safety system approach in Brunei," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 594-607.

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