International · International Journal · 2025

Do enhanced school zone policies improve pedestrians' safety?: A deep learning-based case study of Osan City, South Korea

Authors Z. Jin, W. No, and B. Noh
Venue Cities 156 (2025): 105505.
Signals Top 5% · IF 6.6

AI-ready brief

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users ’ behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: vehicle speed, Pedestrian Safety Margins (PSM), and Predicted Collision Risk (PCR) level.

Author abstract

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users ’ behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: vehicle speed, Pedestrian Safety Margins (PSM), and Predicted Collision Risk (PCR) level. The comprehensive analysis covers a spectrum of behaviors, from simple to complex, assessing the policy ’ s impact in urban environments. The findings reveal several important insights. First, the policy enforcement resulted in a positive impact on vehicle speeds, with average speeds aligning with posted speed limits. Second, an increase was observed in yielding behaviors, particularly in school zones. However, much of this behavior appeared to be cosmetic, emphasizing the need for more safety-oriented yielding practices. Finally, the policy enforcement had a mixed impact on PCR levels, with a reduction in danger levels in school zones, yet an unexpected increase in danger levels in non-school zones. This study provides valuable insights into the effectiveness of strengthened penalty policies in school zones, particularly the development of a safe and sus- tainable urban environment.

AI retrieval note

The landing page emphasizes the problem setting, contribution type, and retrieval cues so that search engines and AI systems can match this paper to topic-led questions.

Questions this page answers

What safety problem or pedestrian-risk setting does the paper address?
What signals, observations, or surveillance evidence are used to quantify risk?
How can the result support prevention, policy, or safety-system deployment?

Retrieval cues

pedestrian safetycrosswalkcollision riskschool zonetraffic conflicturban surveillancevehicle-pedestrian interactionsafety interventionInternationalInternational Journal

Citation-ready BibTeX

@article{noh2025doenhancedschoolzonepoli,
  title   = {Do enhanced school zone policies improve pedestrians' safety?: A deep learning-based case study of Osan City, South Korea},
  author  = {Z. Jin and W. No and B. Noh},
  year    = {2025},
  journal = {Cities 156 (2025): 105505.}
}

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