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<p>Tackling the global challenge of road safety, which tragically continues to take a significant toll on lives, requires innovative strategies based on new technologies such as Artificial Intelligence and the Internet of Things, among others. This paper introduces a comprehensive approach that blends subjective citizen perceptions with objective data for thorough road risk analysis. Providing a multifaceted perspective on road safety aims to enhance risk management effectively. The approach focuses on understanding current risks through risk assessment and anticipating future risks via predictive analysis, both crucial for proactive risk mitigation. Additionally, it offers nuanced insights into road risk dynamics, helping recommend safer routes and refine urban planning strategies. Employing explainable machine and deep learning techniques ensures transparency in risk factors, thereby transforming road safety policies and practices. This integrated approach shows potential for yielding positive outcomes in both communities and urban environments.</p>

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Recommendation and explanation in road safety: a holistic exploration of multifaceted road risk

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Ibtissem Khedher,
  • Noura Faci,
  • Souhayel Gazzah,
  • Sami Faiz

摘要

Tackling the global challenge of road safety, which tragically continues to take a significant toll on lives, requires innovative strategies based on new technologies such as Artificial Intelligence and the Internet of Things, among others. This paper introduces a comprehensive approach that blends subjective citizen perceptions with objective data for thorough road risk analysis. Providing a multifaceted perspective on road safety aims to enhance risk management effectively. The approach focuses on understanding current risks through risk assessment and anticipating future risks via predictive analysis, both crucial for proactive risk mitigation. Additionally, it offers nuanced insights into road risk dynamics, helping recommend safer routes and refine urban planning strategies. Employing explainable machine and deep learning techniques ensures transparency in risk factors, thereby transforming road safety policies and practices. This integrated approach shows potential for yielding positive outcomes in both communities and urban environments.