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<p>The persistent rise in road accidents across Tunisia underscores the critical need to improve traffic conditions and empower official organizations with tools for rapid, targeted interventions. To address this challenge, we have developed an innovative framework that combines advanced analytics with intuitive visualization to transform how road risks are assessed and managed. At the core of this approach lies S-Road, a sophisticated algorithm that evaluates traffic severity. Building upon these assessments, we employ machine learning techniques such as DBSCAN to regroup roads into distinct risk categories, from low to critical. This clustering not only identifies high-risk roads but also reveals hidden patterns in accident causation, enabling authorities to prioritize interventions where they are most needed. To ensure actionable outcomes, we complement this analysis with an interactive, color-coded visualization system, where roads are dynamically mapped according to their danger level. This intuitive dashboard allows decision-makers to quickly pinpoint hazardous segments, simulate the impact of potential safety measures, and allocate resources efficiently. </p>

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An Analytical Framework for Clustering Roads Based on Crash Severity Analysis

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Ibtissem khedher,
  • Neila Rjaibi,
  • Marwen khedher,
  • Sami Faiz

摘要

The persistent rise in road accidents across Tunisia underscores the critical need to improve traffic conditions and empower official organizations with tools for rapid, targeted interventions. To address this challenge, we have developed an innovative framework that combines advanced analytics with intuitive visualization to transform how road risks are assessed and managed. At the core of this approach lies S-Road, a sophisticated algorithm that evaluates traffic severity. Building upon these assessments, we employ machine learning techniques such as DBSCAN to regroup roads into distinct risk categories, from low to critical. This clustering not only identifies high-risk roads but also reveals hidden patterns in accident causation, enabling authorities to prioritize interventions where they are most needed. To ensure actionable outcomes, we complement this analysis with an interactive, color-coded visualization system, where roads are dynamically mapped according to their danger level. This intuitive dashboard allows decision-makers to quickly pinpoint hazardous segments, simulate the impact of potential safety measures, and allocate resources efficiently.