Research article Special Issues

Intelligent traffic safety cloud supervision system based on Internet of vehicles technology

  • Received: 12 July 2023 Revised: 17 September 2023 Accepted: 26 September 2023 Published: 11 October 2023
  • In view of the poor supervision effect of the traditional monitoring cloud supervision system, this paper puts forward a design method of Intelligent Transportation Security Cloud supervision system based on the Internet of vehicles technology, uses the tsed-01 sensor chip to optimize the hardware configuration of the cloud supervision system, perfects the software functions based on the Internet of vehicles technology, and relies on the Internet of vehicles communication platform and cloud data sharing equipment to optimize the software functions of the cloud supervision system, identify and manage the heterogeneous data sources generated by different modules in the cloud supervision system to simplify the steps of the cloud supervision system and provide data support for the comprehensive decision-making of traffic management. The experimental results show that the intelligent traffic safety cloud supervision system based on the Internet of vehicles technology has good practicability, and has guiding significance for the construction of urban rail transit monitoring cloud supervision systems in the future.

    Citation: Jian Gao, Hao Liu, Yang Zhang. Intelligent traffic safety cloud supervision system based on Internet of vehicles technology[J]. Electronic Research Archive, 2023, 31(11): 6564-6584. doi: 10.3934/era.2023332

    Related Papers:

  • In view of the poor supervision effect of the traditional monitoring cloud supervision system, this paper puts forward a design method of Intelligent Transportation Security Cloud supervision system based on the Internet of vehicles technology, uses the tsed-01 sensor chip to optimize the hardware configuration of the cloud supervision system, perfects the software functions based on the Internet of vehicles technology, and relies on the Internet of vehicles communication platform and cloud data sharing equipment to optimize the software functions of the cloud supervision system, identify and manage the heterogeneous data sources generated by different modules in the cloud supervision system to simplify the steps of the cloud supervision system and provide data support for the comprehensive decision-making of traffic management. The experimental results show that the intelligent traffic safety cloud supervision system based on the Internet of vehicles technology has good practicability, and has guiding significance for the construction of urban rail transit monitoring cloud supervision systems in the future.



    加载中


    [1] E. Namazi, J. Li, C. Lu, Intelligent intersection management systems considering autonomous vehicles: A systematic literature review, IEEE Access, 7 (2019), 91946–91965. https://doi.org/10.1109/ACCESS.2019.2927412 doi: 10.1109/ACCESS.2019.2927412
    [2] U. K. Lilhore, A. L. Imoize, C. T. Li, S. Simaiya, S. K. Pani, N. Goyal, et al., Design and implementation of an ML and IoT based adaptive traffic-management system for smart cities, Sensors, 22 (2022), 2908. https://doi.org/10.3390/s22082908 doi: 10.3390/s22082908
    [3] R. Ravish, S. R. Swamy, Intelligent traffic management: A review of challenges, solutions, and future perspectives, Transport Telecommun. J., 22 (2021), 163–182. https://doi.org/10.2478/ttj-2021-0013 doi: 10.2478/ttj-2021-0013
    [4] S. Sharma, S. K. Awasthi, Introduction to intelligent transportation system: overview, classification based on physical architecture, and challenges, Int. J. Sens. Netw., 38 (2022), 215–240. https://doi.org/10.1504/IJSNET.2022.122593 doi: 10.1504/IJSNET.2022.122593
    [5] K. Jurczenia, J. Rak, A survey of vehicular network systems for road traffic management, IEEE Access, 10 (2022), 42365–42385. https://doi.org/10.1109/ACCESS.2022.3168354 doi: 10.1109/ACCESS.2022.3168354
    [6] A. Dureja, S. Sangwan, Intelligent traffic management system using Ant Colony Optimization and Internet of Things, Int. J. Commun. Syst., 35(2022), e5248.
    [7] J. Lian, Y. Zhou, Model predictive control of the fuel cell cathode system based on state quantity estimation, Comput. Simul., 37 (2020), 119–122. https://doi.org/10.3969/j.issn.1006-9348.2020.07.023 doi: 10.3969/j.issn.1006-9348.2020.07.023
    [8] Y. Lin, Spoken instruction understanding in air traffic control: challenge, technique, and application, Aerospace, 8 (2021). https://doi.org/10.3390/aerospace8030065 doi: 10.3390/aerospace8030065
    [9] C. Chen, S. Quan, A summary of security techniques-based blockchain in IoV, Secur. Commun. Netw., 2022 (2022), 8689651. https://doi.org/10.1155/2022/8689651 doi: 10.1155/2022/8689651
    [10] T. K. Chan, C. S. Chin, Review of autonomous intelligent vehicles for urban driving and parking, Electronics, 10 (2021), 1021. https://doi.org/10.3390/electronics10091021 doi: 10.3390/electronics10091021
    [11] N. Sharma, N. Chauhan, N. Chand, Cluster based distributed service discovery in Internet of vehicle, J. Commun. Software Syst., 17 (2021), 281–288. https://doi.org/10.24138/jcomss-2021-0069 doi: 10.24138/jcomss-2021-0069
    [12] H. Zhang, L. Zhang, Y. Guo, Z. Wang, Data transmission mechanism of vehicle networking based on fuzzy comprehensive evaluation, Open Math., 20 (2022), 1909–1925. https://doi.org/10.1515/math-2022-0537 doi: 10.1515/math-2022-0537
    [13] C. Xu, H. Wu, H. Liu, W. Gu, Y. Li, D. Cao, Blockchain-oriented privacy protection of sensitive data in the internet of vehicles, IEEE Trans. Intell. Veh., 8 (2022), 1057–1067. https://doi.org/10.1109/TIV.2022.3164657 doi: 10.1109/TIV.2022.3164657
    [14] C. Zhang, M. Dong, K. Ota, Employ AI to improve AI services: Q-learning based holistic traffic control for distributed co-inference in deep learning, IEEE Trans. Serv. Comput., 15 (2022), 627–639. https://doi.org/10.1109/TSC.2021.3113184 doi: 10.1109/TSC.2021.3113184
    [15] Z. Wang, Y. Ma, Detection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system, Neural Comput. Appl., 34 (2021), 3513–3522. https://doi.org/10.1007/s00521-021-05870-6 doi: 10.1007/s00521-021-05870-6
    [16] R. Markowska, Z. Wróbel, Selected issues of safe operation of the railway traffic control system in the event of exposition to damage caused by lightning discharges, Energies, 14 (2021). https://doi.org/10.3390/en14185808 doi: 10.3390/en14185808
    [17] S. Hoskova-Mayerova, J. Kalvoda, M. Bauer, P. Rackova, Development of a methodology for assessing workload within the air traffic control environment in the Czech Republic, Sustainability, 14 (2022). https://doi.org/10.3390/su14137858 doi: 10.3390/su14137858
    [18] A. Vernotte, A. Cretin, B. Legeard, F. Peureux, A domain-specific language to design false data injection tests for air traffic control systems, Int. J. Software Tools Technol. Transfer, 24 (2021), 127–158. https://doi.org/10.1007/s10009-021-00604-4 doi: 10.1007/s10009-021-00604-4
    [19] C. Li, S. Xi, C. Lu, C. D. Gill, R. Guerin, Prioritizing soft real-time network traffic in virtualized hosts based on xen, in 21st IEEE Real-Time and Embedded Technology and Applications Symposium, (2015), 145–156. https://doi.org/10.1109/TNET.2021.3114055
    [20] L. Dzhuma, O. Dmitriiev, O. Lavrynenko, M. Soroka, Revealing the regularities related to the professional activities of the air traffic controller of airport traffic control tower, Technol. Audit Prod. Reserves, 3 (2021), 29–40. https://doi.org/10.15587/2706-5448.2021.235456 doi: 10.15587/2706-5448.2021.235456
    [21] Q. Xu, Y. Pang, Y. Liu, Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN), Transp. Res. Part C Emerging Technol., 153 (2023). https://doi.org/10.1016/j.trc.2023.104225 doi: 10.1016/j.trc.2023.104225
    [22] S. Borsuk, O. Reva, Distributions of air traffic control students' attitudes towards workload, Aviation, 25 (2021), 241–251. https://doi.org/10.3846/aviation.2021.15954 doi: 10.3846/aviation.2021.15954
    [23] M. E. S. Ciu, A. R. Ub, The implementation of e-government in the sector transportation (studi on area traffic control system program resources in Sidoarjo district), JKMP, 10 (2022), 54–63. https://doi.org/10.21070/jkmp.v10i1.1688 doi: 10.21070/jkmp.v10i1.1688
    [24] C. J. Qun, On-ramp traffic control and optimization of urban expressway, J. Syst. Manage., 31 (2022), 27. https://doi.org/10.3969/j.issn1005-2542.2022.01.003 doi: 10.3969/j.issn1005-2542.2022.01.003
    [25] J. A. Chris, Potential use and benefits of automation for traffic control in roadway construction, J. Civil Eng. Constr. Technol., 12 (2021), 7–24. https://doi.org/10.5897/JCECT2020.0549 doi: 10.5897/JCECT2020.0549
    [26] D. Fuscá, K. Rahimli, R. Leuzzi, Identification of vessel class with LSTM using kinematic features in maritime traffic control, Int. J. Mar. Environ. Sci., 16 (2022).
    [27] P. Wang, P. Li, F. R. Chowdhury, Development of an adaptive traffic signal control framework for urban signalized interchanges based on infrastructure detectors and CAV technologies, J. Transp. Eng. Part A. Syst., 148 (2022). https://doi.org/10.1061/JTEPBS.0000648 doi: 10.1061/JTEPBS.0000648
    [28] I. K. U. Adizov, Operational traffic control with a system of priorities at stations without centralization, Sci. Prog., 2 (2021), 797–803.
    [29] S. Neelakandan, M. A. Berlin, S. Tripathi, V. B. Devi, I. Bhardwaj, N. Arulkumar, IoT-based traffic prediction and traffic signal control system for smart city, Soft Comput., 25 (2021), 12241–12248. https://doi.org/10.1007/s00500-021-05896-x doi: 10.1007/s00500-021-05896-x
    [30] D. Li, S. B. De, Distributed model-free adaptive predictive control for urban traffic networks, IEEE Trans. Control Syst. Technol., 30 (2022), 180–192. https://doi.org/10.1109/TCST.2021.3059460 doi: 10.1109/TCST.2021.3059460
    [31] T. Yoshioka, H. Sakakibara, R. Tenhagen, S. Lorkowski, T. Oguchi, Traffic signal control parameter calculation using probe data, Int. J. Intell. Transp. Syst. Res., 20 (2022), 288–298. https://doi.org/10.1007/s13177-021-00292-z doi: 10.1007/s13177-021-00292-z
    [32] Y. Ju, Y. Chen, Z. Cao, L. Liu, Q. Pei, M. Xiao, et al., Joint secure offloading and resource allocation for vehicular edge computing network: A multi-agent deep reinforcement learning approach, IEEE Trans. Intell. Transp. Syst., 24 (2023), 5555–5569. https://doi.org/10.1109/TITS.2023.3242997 doi: 10.1109/TITS.2023.3242997
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(841) PDF downloads(90) Cited by(0)

Article outline

Figures and Tables

Figures(10)  /  Tables(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog