TRAFFIC MITIGATION AND VEHICLE DETECTION BASED ON HOMOMORPHIC ENCRYPTION ALGORITHM AND FUZZY COMPREHENSIVE EVALUATION

Zihao Chen∗

References

  1. [1] Z. Yang, H. Wang, and B. Chen, Assessment of urbanwaterlogging-induced road traffic safety risk and identificationof its driving factors: A case study of Beijing, Trans-portation Research Part A: Policy and Practice, 183, 2024,104080.
  2. [2] Y. Pan, Y.Wu, L. Xu, C. Xia, and D.L. Olson, The impactsof connected autonomous vehicles on mixed traffic flow: Acomprehensive review, Physica A: Statistical Mechanics andits Applications, 635, 2024, 129454.
  3. [3] C. Tan, and K. Yang, Privacy-preserving adaptive trafficsignal control in a connected vehicle environment, Trans-portation Research Part-C: Emerging Technologies, 158, 2024,104453.
  4. [4] F. Nadipour, S. Sedaghat, E. Amiri, and M.S. Rastad,A deep-learning-based SIoV framework in vehicle detectionand counting system for Intelligent traffic management, inProceeding 8th International Conference on Smart Cities,Internet of Things and Applications (SCIoT), Mashhad, 2024,49–54.
  5. [5] H. Xu, A. Berres, S.A. Tennille, S.K. Ravulaparthy, C.Wang, and J. Sanyal, Continuous emulation and multiscalevisualization of traffic flow using stationary roadside sensordata, IEEE Transactions on Intelligent TransportationSystems, 23(8), 2021, 10530–10541.
  6. [6] R. Al-Huthaifi, T. Li, Z. Al-Huda, W. Huang, Z. Luo,and P. Xie, FedGODE: Secure traffic flow predictionbased on federated learning and graph ordinary differentialequation networks, Knowledge-Based Systems, 299, 2024,112029.
  7. [7] Y. Feng, S.E. Huang, W. Wong, Q.A. Chen, Z.M. Mao,and HX. Liu, On the cybersecurity of traffic signal controlsystem with connected vehicles, IEEE Transactions onIntelligent Transportation Systems, 23(9), 2022,16267–16279.
  8. [8] S.S. Chaeikar, A. Jolfaei, and N. Mohammad, AI-enabled cryp-tographic key management model for secure communicationsin the Internet of Vehicles, IEEE Transactions on IntelligentTransportation Systems, 24(4), 2022, 4589–4598.
  9. [9] M. Mao, P. Yi, J. Zhang, and J. Pei, Detecting maliciousroadside units in vehicular social networks for informationservice, Wireless Personal Communications, 130(4), 2023,2565–2588.
  10. [10] M.Z. Mehdi, H.M. Kammoun, N.G. Benayed, D. Sellami,and A.D. Masmoudi, Entropy-based traffic flow labeling forCNN-based traffic congestion prediction from meta-parameters,IEEE Access, 10, 2022, 16123–16133.
  11. [11] M. Gillani, H.A. Niaz, A. Ullah, M.U. Farooq, and S. Rehman,Traffic aware data gathering protocol for VANETs, IEEEAccess, 10, 2022, 23438–23449.
  12. [12] B.A.I. Yu, T.U. Pengyue, and G.P. Ong, An extended intelligentdriving model for autonomous and manual driven vehicles ina mixed traffic environment with consideration to roadsidecrossing, International Journal of Transportation Science andTechnology, 17, 2025, 375–391.
  13. [13] G. Araujo, and L. Sampaio, A scalable, dynamic, andsecure traffic management system for vehicular nameddata networking applications, Ad Hoc Networks, 158, 2024,103476.
  14. [14] X. Zhang, J. Lai, and A.J. Moshayedi, Traffic data securitysharing scheme based on blockchain and traceable ring signaturefor VANETs, Peer-to-Peer Networking and Applications, 16(5),2023, 2349–2366.
  15. [15] Q. Wang and K. Yang, Privacy-preserving data fusion for trafficstate estimation: A vertical federated learning approach, 2024,arXiv:2401.11836.
  16. [16] Z. Long, Y. Wang, and Z. Luo, Fuzzy control robot energysaving method based on particle swarm optimisation algorithm,International Journal of Robotics and Automation, 39(6), 2024,482–489.
  17. [17] L. Wang, J. Gao, S. Lang, and W. An, Research on optimalcontrol algorithm of double contraflow left-turn lanes at urbanroad intersections, International Journal of Robotics andAutomation, 38(5), 2023, 367–374.11

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