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Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

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  • Li, Chunjie
  • Xu, Chengcheng
  • Chen, Yusen
  • Li, Zhibin

Abstract

To increase the efficiency and safety of expressway, this paper constructed a new intelligent connected cooperative vehicle infrastructure system and its effectiveness was verifid from both data and practical applications. Firstly, considering the convenience of using intelligent networking systems for public transportation, a new intelligent connected cooperative vehicle infrastructure system architecture was proposed by incorporating mobile communication methods. Then, the new system was illustrated from road side unit (RSU), on board unit (OBU) and data interaction. Additionally, to verify the effectiveness of the system, this paper proposes a two-stage model named Transformer Embedded Clustering- Hierarchical Density-Based Spatial Clustering of Applications with Noise (TEC-HDBSCAN) model to identify outliers in the trajectory data of vehicles collected by the system and obtain the speed sequence of the vehicle. Finally, data from actual testing scenarios was collected and a Best Worst Method-Improved Gray Relational (BWM-IGR) model was built to verify the effectiveness of the system. The results show that the established intelligent networked transportation system can effectively guide vehicles and collect data with high accuracy.

Suggested Citation

  • Li, Chunjie & Xu, Chengcheng & Chen, Yusen & Li, Zhibin, 2024. "Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    • Handle: RePEc:eee:phsmap:v:635:y:2024:i:c:s0378437124000062
    DOI: 10.1016/j.physa.2024.129498
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    References listed on IDEAS

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    1. Wu, Yuanyuan & Wang, David Z.W. & Zhu, Feng, 2022. "Influence of CAVs platooning on intersection capacity under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Cong Zhao & Delong Ding & Zhouyang Du & Yupeng Shi & Guimin Su & Shanchuan Yu, 2023. "Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems," IJERPH, MDPI, vol. 20(1), pages 1-21, January.
    3. Degrande, Thibault & Vannieuwenborg, Frederic & Verbrugge, Sofie & Colle, Didier, 2023. "Deployment of Cooperative Intelligent Transport System infrastructure along highways: A bottom-up societal benefit analysis for Flanders," Transport Policy, Elsevier, vol. 134(C), pages 94-105.
    4. Zhao, Jiandong & Yu, Zhixin & Yang, Xin & Gao, Ziyou & Liu, Wenhui, 2022. "Short term traffic flow prediction of expressway service area based on STL-OMS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
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