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The policy brief analyzes limitations of relying on experimental and observational studies that require extensive crash-data collection. It proposes a framework that mines CMF Clearinghouse records to uncover previously unidentified relationships, using heterogeneous data and countermeasure context to deliver cost-effective, time-efficient preliminary CMF predictions. Results show reasonable prediction accuracy and flexibility, while highlighting needs for improved output confidence reporting.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-can-reveal-effectiveness-of-traffic-safety-countermeasures/290641/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-can-reveal-effectiveness-of-traffic-safety-countermeasures/290641.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-20","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Why do practitioners need better methods to estimate CMFs?","Question",{"text":112,"@type":113},"Practitioners require CMFs to assess countermeasures, but available CMFs often do not cover all scenarios of interest for State DOTs, especially when projects involve novel infrastructure types or countermeasures.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are the dominant approaches for estimating CMFs, and what are their drawbacks?",{"text":117,"@type":113},"Experimental and observational studies are dominant. Experimental research is rigorous but requires years to collect sufficient crash data, while observational approaches can be less ethically burdensome but still depend on substantial crash-data availability.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed machine learning framework use existing data?",{"text":121,"@type":113},"The framework mines CMF Clearinghouse data to uncover previously unidentified relationships, fully exploring existing CMF records and incorporating heterogeneous data while capturing semantic contexts of countermeasures.",{"name":123,"@type":110,"acceptedAnswer":124},"Can the machine learning approach replace traditional CMF development methods?",{"text":125,"@type":113},"No. The approach is intended to provide preliminary CMF estimates for quick screening and prioritization, complementing traditional methods rather than replacing them.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},290641,1789642256,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":133,"read_time":39},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","UC Office of the President  \nPolicy Briefs  \nTitle  \nMachine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures  \nPermalink  \n[https://escholarship.org/uc/item/0x26t67j](https://escholarship.org/uc/item/0x26t67j)  \nAuthors  \nLi, Jia, PhD  \nQi, Yanlin  \nZhang, Michael, PhD  \nPublication Date  \n2025-08-01  \nDOI  \n10.7922/G2VD6WVF  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPOLICY BRIEF Institute of  \nTransportation Studies  \nMachine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures  \nJia Li, Ph.D., Department of Civil and Environmental Engineering,  \nWashington State University  \nYanlin Qi, Institute of Transportation Studies*  \nMichael Zhang, Ph.D., Department of Civil and Environmental Engineering*  \n*University of California, Davis August 2025  \nIssue  \nEmerging 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.  \nThe US Department of Transportation’s (DOT) CMF Clearinghouse provides practitioners with a list of reliable CMFs developed from individual studies. However, available CMFs do not cover all potential scenarios-of-interest to State DOTs because unique projects may feature novel infrastructure types or countermeasures. Experimental or observational studies are the dominant tools for estimating CMFs. However, these approaches may require years of effort to collect adequate crash data.  \nTo address these challenges, we developed a machine learning framework that mines CMF Clearinghouse data to uncover previously unidentified relationships. This provides a cost-effective and time-efficient solution to assessing CMFs not covered by the CMF Clearinghouse. Our proposed framework fully explores existing CMF data.  \nWe extensively trained and tested the proposed approach on CMF Clearinghouse data with experiments and showed that the framework can predict CMFs with reasonable accuracy. The framework flexibly incorporates heterogeneous data from the CMF Clearinghouse, captures the semantic contexts of countermeasures, and maintains data compatibility.  \nKey Research Findings  \nThe two dominant approaches for estimating CMFs are experimental or observational studies based on before/after or cross-sectional data. Experimental studies are rigorous, requiring years of effort to collect data. This long study cycle hinders wide applications. Observational studies, in contrast, bring fewer ethical concerns than experimentation and can leverage data from sites with already-implemented treatment(s). Before-after studies typically require multiyear crash data collected before and after a treatment. Most cross-sectional studies require crash inventory data collected from multiple sites with and without a treatment in a single period. Both types of studies require substantial quantities of crash data.  \nA machine-learning framework can mine CMF Clearinghouse data. As a one-stop repository, the CMF Clearinghouse has long been used as a search database to support practitioners responsible for infrastructure safety. With thousands of existing CMF records and continuing  \n[www. uc its. org](www. uc its. org)  \nInstitute of Transportation Studies     \nentries, the CMF Clearinghouse has tremendous potential as a knowledge base for deriving additional CMFs. Our knowledge-mining approach for deriving additional CMFs uses information already available, thereby reducing the data-collection and study duration burden of es","cbCaivoU16H3gpyJ","https://ap.wps.com/l/cbCaivoU16H3gpyJ","pdf",334438,"English","# Issue\n# Key Research Findings\n## Experimental and observational study approaches\n## Learning-based CMF estimation\n# Policy Recommendations\n# More Information","[{\"question\":\"Why do practitioners need better methods to estimate CMFs?\",\"answer\":\"Practitioners require CMFs to assess countermeasures, but available CMFs often do not cover all scenarios of interest for State DOTs, especially when projects involve novel infrastructure types or countermeasures.\"},{\"question\":\"What are the dominant approaches for estimating CMFs, and what are their drawbacks?\",\"answer\":\"Experimental and observational studies are dominant. Experimental research is rigorous but requires years to collect sufficient crash data, while observational approaches can be less ethically burdensome but still depend on substantial crash-data availability.\"},{\"question\":\"How does the proposed machine learning framework use existing data?\",\"answer\":\"The framework mines CMF Clearinghouse data to uncover previously unidentified relationships, fully exploring existing CMF records and incorporating heterogeneous data while capturing semantic contexts of countermeasures.\"},{\"question\":\"Can the machine learning approach replace traditional CMF development methods?\",\"answer\":\"No. The approach is intended to provide preliminary CMF estimates for quick screening and prioritization, complementing traditional methods rather than replacing them.\"}]","Machine Learning Can Reveal Effectiveness of Traffic Safety Countermeasures | PDF"]