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Modeling and stabilization control for heterogeneous traffic flow model considering cyberattacks

Author

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  • Ge, Hongxia
  • Lin, Lizhen
  • Cheng, Rongjun

Abstract

With the popularity of computer internet technology in transportation, connected automated vehicles (CAVs) maintain the traffic flow stability by sharing real-time driving information. However, the open-access environment makes CAVs vulnerable to attacks. Hackers transmit erroneous driving information to drivers, which lead to changes in their driving behavior and cause potential traffic hazards. Based on the summary of existing essays, this paper classifies cyberattacks into three types: false messages, repeat/delay and collusion attack. In addition, mixed flow is a common form of traffic. This paper proposed an extended heterogeneous traffic flow model considering cyberattacks, judgments the stability of the CAVs platoon and designs the controller for stability control. Subsequently, the stability of the new model is analyzed theoretically, and the stability condition of the new model is obtained. The simulation results show that the when CAVs platoon attacked by uncertain types and locations, it can still maintain the stability of the team and reduce the time for the team to return to equilibrium.

Suggested Citation

  • Ge, Hongxia & Lin, Lizhen & Cheng, Rongjun, 2023. "Modeling and stabilization control for heterogeneous traffic flow model considering cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    • Handle: RePEc:eee:phsmap:v:622:y:2023:i:c:s0378437123003540
    DOI: 10.1016/j.physa.2023.128799
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Cheng, Rongjun & Ji, Qun & Zheng, Yuchen & Ge, Hongxia, 2023. "Analysis of the impact of cyberattacks on the lane changing behavior of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Yadav, Sunita & Redhu, Poonam, 2024. "Impact of driving prediction on headway and velocity in car-following model under V2X environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Zhai, Cong & Wu, Weitiao & Xiao, Yingping & Zhang, Jiyong & Zhai, Min & Wu, Yingzi, 2025. "A novel throttle-based self-stabilizing control scheme integrated into an anisotropic continuum model to mitigate cyber-attacks in connected vehicle scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 201(P1).
    5. Tan, Huili & Sun, Yuanlong & Xia, Dongxue & Peng, Guanghan, 2025. "Congestion transition in intelligent driver model with fault-tolerant control to counter cyber-attacks during the lane-changing process under a connected autonomous vehicles platform," Chaos, Solitons & Fractals, Elsevier, vol. 199(P3).
    6. Tang, Yi & Zhai, Cong & Xiao, Yingping & Zhai, Min & Zhang, Jiyong, 2025. "Effect of malicious cyber-attack on jamming transition in a multi-phase mixed flow model under regular vehicles and connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    7. Jin, Can & Peng, Guanghan & Huang, Yixin, 2025. "Phase transitions in operation of heterogeneous vehicles mixed with human-driven and connected autonomous vehicles under speed restriction circumstances," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    8. Guan, Xueyi & Qin, Jin & Cheng, Rongjun & Wang, Ting, 2025. "Bifurcation and linear analysis of connected heterogeneous traffic flow model under cyberattacks," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).

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