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<p>Road traffic crashes involving trucks pose a significant transportation safety issue due to their impact on injury severity. While extensive research exists on truck-related injury severity, limited studies explore the interplay of risk factors affecting long-haul truck drivers (LHTDs), particularly in developing countries like India. The present study employs apriori algorithm from association rules mining (ARM) technique to uncover hidden patterns of risk factors influencing injury severity among LHTDs in India. Further, the study proposes strategies from the perspectives of LHTDs to improve safety. Self-reported injury severity data of 214 LHTDs were obtained by face-to-face interviews employing a questionnaire in Salem, Tamil Nadu, India. Injury severity levels considered include no injury/property damage, minor injury, and major injury. Various driver, work, vehicle, health, and crash-related characteristics were examined. The results from ARM analysis reveal that (1) morning crashes involving illegal overtaking or hitting animals were associated with no injury; (2) LHTDs sleeping 5–6&#xa0;h and driving 25–31-tonne trucks had higher association with minor injury; and (3) speeding and bus collisions were strongly associated with major injuries. The analysis on preferences of safety strategies indicated that drivers with major injuries and no injuries predominantly favored post-crash emergency systems at 27.65% and 29.87% respectively. On the other hand, LHTDs with minor injuries mostly preferred dedicated freight corridors, accounting for 32.55%. These findings offer crucial insights for Indian trucking firms, road safety authorities, and policymakers to develop targeted injury prevention strategies.</p>

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Examining factors influencing injury severity patterns and proposing safety strategies for long-haul truck drivers in India: an association rule mining approach

  • 期刊论文

  • 发表日期：

    2025年9月20日

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  • Balamurugan Shandhana Rashmi,
  • Sankaran Marisamynathan

摘要

Road traffic crashes involving trucks pose a significant transportation safety issue due to their impact on injury severity. While extensive research exists on truck-related injury severity, limited studies explore the interplay of risk factors affecting long-haul truck drivers (LHTDs), particularly in developing countries like India. The present study employs apriori algorithm from association rules mining (ARM) technique to uncover hidden patterns of risk factors influencing injury severity among LHTDs in India. Further, the study proposes strategies from the perspectives of LHTDs to improve safety. Self-reported injury severity data of 214 LHTDs were obtained by face-to-face interviews employing a questionnaire in Salem, Tamil Nadu, India. Injury severity levels considered include no injury/property damage, minor injury, and major injury. Various driver, work, vehicle, health, and crash-related characteristics were examined. The results from ARM analysis reveal that (1) morning crashes involving illegal overtaking or hitting animals were associated with no injury; (2) LHTDs sleeping 5–6 h and driving 25–31-tonne trucks had higher association with minor injury; and (3) speeding and bus collisions were strongly associated with major injuries. The analysis on preferences of safety strategies indicated that drivers with major injuries and no injuries predominantly favored post-crash emergency systems at 27.65% and 29.87% respectively. On the other hand, LHTDs with minor injuries mostly preferred dedicated freight corridors, accounting for 32.55%. These findings offer crucial insights for Indian trucking firms, road safety authorities, and policymakers to develop targeted injury prevention strategies.