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Machine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures

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  • Li, Jia PhD
  • Qi, Yanlin
  • Zhang, Michael PhD

Abstract

Emerging machine learning capabilities can be leveraged to make transportation infrastructure safer and reduce fatalities by informing decisions about which countermeasures to apply at crash-prone locations. At this time, project prioritization typically involves assessing effectiveness, cost-benefit ratios, and available funding. Crash Modification Factors (CMFs) play an essential role in project assessment by predicting the effectiveness of safety countermeasures. Their applicability has limitations, however. Some of these may be overcome with innovative approaches such as knowledge-mining.

Suggested Citation

  • Li, Jia PhD & Qi, Yanlin & Zhang, Michael PhD, 2025. "Machine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures," Institute of Transportation Studies, Working Paper Series qt0x26t67j, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt0x26t67j
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