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<p>The significant increase in road crashes highlights the need to evaluate road safety in an emerging country like India. The policy initiatives and technological developments implemented by policy-makers to enhance traffic safety are critical for the country’s overall development. In this context, the present study aims to conduct Data Envelopment Analysis (DEA) and classify Indian states and Union Territories (UT) as Decision-Making Units (DMUs) based on reported traffic crash data. The required data for this study were collected from the Ministry of Road Transport and Highways (MoRTH), Reserve Bank of India (RBI), and Basic Road Statistics of India (BRS). The DEA analysis was then performed using RStudio software for developing the Charnes Cooper Rhodes (CCR) and Banker Charnes Cooper (BCC) models. An output-oriented strategy was used to minimize traffic fatalities and injuries per 10,000 nationally registered vehicles. The results indicated that the BCC model outperforms than CCR models in determining the effective frontiers from the selected DMUs. Therefore, the corresponding DMUs are organized into hierarchical clusters using a dendrogram to determine the best class of the respective cluster. These results are useful for the state governments of the respective states as they can identify the less efficient DMUs based on the DEA scores. By implementing targeted interventions, the likelihood of crashes can be reduced, leading to a decrease in the severity of injuries and fatalities.</p>

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Evaluating Indian Provincial Road Safety Performance Based on Traffic Crashes

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Anjana Ramesh,
  • Riya Thomas,
  • Jaydip Goyani,
  • Shriniwas Arkatkar

摘要

The significant increase in road crashes highlights the need to evaluate road safety in an emerging country like India. The policy initiatives and technological developments implemented by policy-makers to enhance traffic safety are critical for the country’s overall development. In this context, the present study aims to conduct Data Envelopment Analysis (DEA) and classify Indian states and Union Territories (UT) as Decision-Making Units (DMUs) based on reported traffic crash data. The required data for this study were collected from the Ministry of Road Transport and Highways (MoRTH), Reserve Bank of India (RBI), and Basic Road Statistics of India (BRS). The DEA analysis was then performed using RStudio software for developing the Charnes Cooper Rhodes (CCR) and Banker Charnes Cooper (BCC) models. An output-oriented strategy was used to minimize traffic fatalities and injuries per 10,000 nationally registered vehicles. The results indicated that the BCC model outperforms than CCR models in determining the effective frontiers from the selected DMUs. Therefore, the corresponding DMUs are organized into hierarchical clusters using a dendrogram to determine the best class of the respective cluster. These results are useful for the state governments of the respective states as they can identify the less efficient DMUs based on the DEA scores. By implementing targeted interventions, the likelihood of crashes can be reduced, leading to a decrease in the severity of injuries and fatalities.