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Uncovering contextual risk patterns in cannabis-involved fatal crashes: A data-driven approach to public health-oriented road safety.

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Title: Uncovering contextual risk patterns in cannabis-involved fatal crashes: A data-driven approach to public health-oriented road safety.
Authors: Chakraborty, Rohit1 (AUTHOR) rohitchakraborty@txstate.edu, Bashar Polock, Sazzad Bin1 (AUTHOR) pay28@txstate.edu, Pandey, Biplov1 (AUTHOR) iub14@txstate.edu, Shuvo, Sawgat Ahmed1 (AUTHOR) ayo28@txstate.edu, Dey, Kakan2 (AUTHOR) kakan@msu.edu, Das, Subasish1 (AUTHOR) subasish@txstate.edu
Source: Journal of Safety Research. Feb2026, Vol. 96, p160-173. 14p.
Subjects: Drugged driving, Public health, Dimensional reduction algorithms, Traffic safety, Road safety measures, Collisions (Physics)
Geographic Terms: United States
Abstract: Introduction : As cannabis legalization expands across the United States, concerns about its impact on road safety and public health continue to grow. This study examined fatal crashes involving cannabis-involved drivers using national data from the Fatality Analysis Reporting System (FARS) between 2018 and 2022, focusing on cases where cannabis was toxicologically confirmed in the driver's bloodstream. Method: To uncover underlying crash typologies, we applied Cluster Correspondence Analysis (CCA), a two-way dimension reduction method optimized for categorical data, to reveal patterns across roadway environments, driver demographics, crash dynamics, and environmental conditions. Results: The analysis revealed six distinct crash clusters: rural straight-roadway single-vehicle collisions, high-speed multi-vehicle crashes with lane conflicts, single-vehicle crashes on curves with loss of control, turning and yielding errors at intersections, unusual user and road conditions with pedestrian involvement, and nighttime urban crashes involving vulnerable road users. These findings highlighted the intersection of tetrahydrocannabinol (THC)-positive toxicology and systemic infrastructure vulnerabilities that contribute to fatal outcomes in cannabis-involved crashes. Conclusions and Practical Applications: By using a method designed for complex categorical datasets, this research provided novel insights into the multifaceted risks associated with drug-impaired driving. The results could inform targeted countermeasures, such as improved roadway lighting, intersection design, and behavioral interventions, offering a data-driven foundation for public health–oriented traffic safety strategies. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Safety Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 192003698
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Uncovering contextual risk patterns in cannabis-involved fatal crashes: A data-driven approach to public health-oriented road safety.
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  Data: <searchLink fieldCode="AR" term="%22Chakraborty%2C+Rohit%22">Chakraborty, Rohit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rohitchakraborty@txstate.edu</i><br /><searchLink fieldCode="AR" term="%22Bashar+Polock%2C+Sazzad+Bin%22">Bashar Polock, Sazzad Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pay28@txstate.edu</i><br /><searchLink fieldCode="AR" term="%22Pandey%2C+Biplov%22">Pandey, Biplov</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> iub14@txstate.edu</i><br /><searchLink fieldCode="AR" term="%22Shuvo%2C+Sawgat+Ahmed%22">Shuvo, Sawgat Ahmed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ayo28@txstate.edu</i><br /><searchLink fieldCode="AR" term="%22Dey%2C+Kakan%22">Dey, Kakan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> kakan@msu.edu</i><br /><searchLink fieldCode="AR" term="%22Das%2C+Subasish%22">Das, Subasish</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> subasish@txstate.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Safety+Research%22">Journal of Safety Research</searchLink>. Feb2026, Vol. 96, p160-173. 14p.
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  Data: Introduction : As cannabis legalization expands across the United States, concerns about its impact on road safety and public health continue to grow. This study examined fatal crashes involving cannabis-involved drivers using national data from the Fatality Analysis Reporting System (FARS) between 2018 and 2022, focusing on cases where cannabis was toxicologically confirmed in the driver's bloodstream. Method: To uncover underlying crash typologies, we applied Cluster Correspondence Analysis (CCA), a two-way dimension reduction method optimized for categorical data, to reveal patterns across roadway environments, driver demographics, crash dynamics, and environmental conditions. Results: The analysis revealed six distinct crash clusters: rural straight-roadway single-vehicle collisions, high-speed multi-vehicle crashes with lane conflicts, single-vehicle crashes on curves with loss of control, turning and yielding errors at intersections, unusual user and road conditions with pedestrian involvement, and nighttime urban crashes involving vulnerable road users. These findings highlighted the intersection of tetrahydrocannabinol (THC)-positive toxicology and systemic infrastructure vulnerabilities that contribute to fatal outcomes in cannabis-involved crashes. Conclusions and Practical Applications: By using a method designed for complex categorical datasets, this research provided novel insights into the multifaceted risks associated with drug-impaired driving. The results could inform targeted countermeasures, such as improved roadway lighting, intersection design, and behavioral interventions, offering a data-driven foundation for public health–oriented traffic safety strategies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Safety Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192003698
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.jsr.2025.12.005
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 160
    Subjects:
      – SubjectFull: Drugged driving
        Type: general
      – SubjectFull: Public health
        Type: general
      – SubjectFull: Dimensional reduction algorithms
        Type: general
      – SubjectFull: Traffic safety
        Type: general
      – SubjectFull: Road safety measures
        Type: general
      – SubjectFull: Collisions (Physics)
        Type: general
      – SubjectFull: United States
        Type: general
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      – TitleFull: Uncovering contextual risk patterns in cannabis-involved fatal crashes: A data-driven approach to public health-oriented road safety.
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 96
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