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Explainable machine learning for injury severity prediction in two-wheeler and light motor vehicle (LMV) single-vehicle crashes.

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Title: Explainable machine learning for injury severity prediction in two-wheeler and light motor vehicle (LMV) single-vehicle crashes.
Authors: Sorum, N. G.1 neerogsorum@gmail.com, Sorum, M. G.1
Source: Advances in Transportation Studies. Jul2026, Vol. 69, p153-176. 24p.
Subjects: Shapley Additive Explanations, Machine learning, Motorcycling accidents, Road safety measures, Traffic accidents, Trauma severity indices
Geographic Terms: Imphāl (India), India
Abstract: Road traffic crashes remain a major safety hazard in India, and single-vehicle crashes (SVCs) with twowheelers and light motor vehicles (LMVs) comprise a significant proportion of severe and fatal injuries. Despite their high risk, SVCs, particularly in India's northeastern region, remain relatively underexplored in the literature. In addition, most existing studies rely on classic statistical models that assume linearity and offer limited interpretability of the complex crash mechanisms. To fill these gaps, this study used explainable machine learning (ML) methods to identify the influential factors of injury severity levels (fatal vs. non-fatal) resulting from SVCs involving two-wheelers and LMVs in Imphal city, India. Using ten years of policereported crash data (2011-2020), six ML models were applied, with SHAP-based interpretation used to identify key contributors to injury severity levels in SVCs. Results indicated that different model classes were optimal for two-wheelers and LMVs, reflecting distinct severity mechanisms across vehicle types. The SHAP analysis consistently highlighted temporal factors, young and middle-aged road users, risky driving behaviors, and roadway deficiencies as the most influential determinants of injury severity levels resulting from SVCs. By integrating predictive modeling with interpretability, the study provides actionable insights for road safety policy, supporting targeted enforcement, awareness initiatives for high-risk groups, and prioritization of infrastructure improvements. The proposed framework also offers broader applicability for injury severity analysis of SVCs in other northeastern regions of India. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies 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.)
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  Data: Road traffic crashes remain a major safety hazard in India, and single-vehicle crashes (SVCs) with twowheelers and light motor vehicles (LMVs) comprise a significant proportion of severe and fatal injuries. Despite their high risk, SVCs, particularly in India's northeastern region, remain relatively underexplored in the literature. In addition, most existing studies rely on classic statistical models that assume linearity and offer limited interpretability of the complex crash mechanisms. To fill these gaps, this study used explainable machine learning (ML) methods to identify the influential factors of injury severity levels (fatal vs. non-fatal) resulting from SVCs involving two-wheelers and LMVs in Imphal city, India. Using ten years of policereported crash data (2011-2020), six ML models were applied, with SHAP-based interpretation used to identify key contributors to injury severity levels in SVCs. Results indicated that different model classes were optimal for two-wheelers and LMVs, reflecting distinct severity mechanisms across vehicle types. The SHAP analysis consistently highlighted temporal factors, young and middle-aged road users, risky driving behaviors, and roadway deficiencies as the most influential determinants of injury severity levels resulting from SVCs. By integrating predictive modeling with interpretability, the study provides actionable insights for road safety policy, supporting targeted enforcement, awareness initiatives for high-risk groups, and prioritization of infrastructure improvements. The proposed framework also offers broader applicability for injury severity analysis of SVCs in other northeastern regions of India. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies 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.)
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        Value: 10.53136/979122182735410
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        Text: English
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        PageCount: 24
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      – SubjectFull: Shapley Additive Explanations
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Motorcycling accidents
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      – SubjectFull: Road safety measures
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      – SubjectFull: Traffic accidents
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      – SubjectFull: Trauma severity indices
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      – SubjectFull: Imphāl (India)
        Type: general
      – SubjectFull: India
        Type: general
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      – TitleFull: Explainable machine learning for injury severity prediction in two-wheeler and light motor vehicle (LMV) single-vehicle crashes.
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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