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Traffic congestion and road safety continue to be critical challenges in urban environments, especially at intersections. Cooperative Intersection Collision Avoidance (CICA) systems have emerged as promising solutions to address these challenges. CICA systems leverage Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to enhance safety and optimize traffic flow at intersections. Our research paper presents a novel CICA system specifically designed for unsignalized intersections. It utilizes a graph-based data model, which is particularly effective for encapsulating the complexities of Intelligent Transportation Systems (ITS) data. This system particularly focuses on rapid query processing, which is vital for effective and timely decision-making. Our method’s primary component is an algorithmic framework that analyzes these graph-based representations to identify and prevent potential traffic issues. With an emphasis on query execution time, we have thoroughly evaluated the system. This paves the way for improving road safety and reducing urban traffic congestion.

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Towards an Efficient and Intelligent Graph Model-Based Cooperative Intersection Collision Avoidance Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Safa Batita,
  • Achraf Makni,
  • Ikram Amous

摘要

Traffic congestion and road safety continue to be critical challenges in urban environments, especially at intersections. Cooperative Intersection Collision Avoidance (CICA) systems have emerged as promising solutions to address these challenges. CICA systems leverage Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to enhance safety and optimize traffic flow at intersections. Our research paper presents a novel CICA system specifically designed for unsignalized intersections. It utilizes a graph-based data model, which is particularly effective for encapsulating the complexities of Intelligent Transportation Systems (ITS) data. This system particularly focuses on rapid query processing, which is vital for effective and timely decision-making. Our method’s primary component is an algorithmic framework that analyzes these graph-based representations to identify and prevent potential traffic issues. With an emphasis on query execution time, we have thoroughly evaluated the system. This paves the way for improving road safety and reducing urban traffic congestion.