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<p>Imagine a bustling city where a single accident brings traffic to a standstill, causing delays, frustration, and increased emissions. Traffic accidents remain a major source of congestion and inefficiency in urban road networks, and traditional traffic management systems often lack the responsiveness to handle sudden disruptions in real time. This paper presents an accident-aware dynamic routing framework for vehicular networks using Vehicle-to-Everything (V2X) communication and classical graph-search algorithms. The study systematically evaluates Depth-First Search (DFS), Breadth-First Search (BFS), and A* algorithms under dynamic accident scenarios within a V2X-enabled environment. To enhance heuristic-guided search, a lightweight Dynamic Edge Heuristic Optimizer (DEHO) is introduced, combining travel time, Euclidean distance, and Manhattan distance to support multi-objective routing decisions. The framework is implemented and evaluated using the VEINS simulation platform on a real urban map of New Damietta City. Simulation results show that A* with DEHO significantly outperforms BFS and DFS in complex scenarios, achieving a ~ 18–20% reduction in travel time, a ~ 20–25% decrease in total distance, and a ~ 19–21% reduction in CO₂ emissions, while simpler algorithms remain competitive in low-complexity cases. These findings provide practical insights into algorithm selection for real-time, accident-aware traffic management in V2X systems.</p>

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Accident-aware traffic management in v2x networks: dynamic route optimization using search algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Hossam M. Zohir,
  • Islam M. Ismael,
  • Eman M. El-Gendy,
  • Mahmoud M. Saafan

摘要

Imagine a bustling city where a single accident brings traffic to a standstill, causing delays, frustration, and increased emissions. Traffic accidents remain a major source of congestion and inefficiency in urban road networks, and traditional traffic management systems often lack the responsiveness to handle sudden disruptions in real time. This paper presents an accident-aware dynamic routing framework for vehicular networks using Vehicle-to-Everything (V2X) communication and classical graph-search algorithms. The study systematically evaluates Depth-First Search (DFS), Breadth-First Search (BFS), and A* algorithms under dynamic accident scenarios within a V2X-enabled environment. To enhance heuristic-guided search, a lightweight Dynamic Edge Heuristic Optimizer (DEHO) is introduced, combining travel time, Euclidean distance, and Manhattan distance to support multi-objective routing decisions. The framework is implemented and evaluated using the VEINS simulation platform on a real urban map of New Damietta City. Simulation results show that A* with DEHO significantly outperforms BFS and DFS in complex scenarios, achieving a ~ 18–20% reduction in travel time, a ~ 20–25% decrease in total distance, and a ~ 19–21% reduction in CO₂ emissions, while simpler algorithms remain competitive in low-complexity cases. These findings provide practical insights into algorithm selection for real-time, accident-aware traffic management in V2X systems.