Machine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures

Jia PhD Li, Yanlin Qi and Michael PhD Zhang

Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis

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.

Keywords: Engineering (search for similar items in EconPapers)
Date: 2025-08-01
New Economics Papers: this item is included in nep-cmp, nep-ppm and nep-tre
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