A Deep Learning Approach to Dynamic Traffic Flow Prediction and Management in VANETs
DOI:
https://doi.org/10.32628/IJSRSET2512329Keywords:
Internet of Things, VANET, Deep Neural Network, Machine LearningAbstract
The evolution of the Internet of Things (IoT) facilitates the emergence of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS). A crucial component of ITS is the vehicular ad hoc network (VANET) featuring smart vehicles (SV). This study introduces a dynamic traffic regulation method within VANET utilizing Deep Neural Networks (DNN) and Bat Algorithms (BA). The DNN is employed to direct vehicles through highly congested routes, thereby minimizing average delays and enhancing efficiency. To assess traffic congestion among network nodes, the BA is integrated with the IoT and implemented over VANETs. Experiments were carried out to evaluate the proposed method's effectiveness based on various parameters, including packet delivery ratio, average latency, and throughput, with results compared against several machine learning (ML) algorithms. The simulation outcomes indicate that the proposed approach effectively manages real-time traffic conditions while consuming less energy and incurring lower delays compared to existing techniques.
Downloads
References
Conference on Advanced Communication Jia, D., Lu, K., Wang, J., Zhang, X., & Shen, X. (2015). A survey on platoon-based vehicular cyber-physical systems. IEEE communications surveys & tutorials, 18(1), 263-284.
A. Yang, J. Weng, K. Yang, C. Huang, and X. Shen, “Delegating authentication to edge: A decentralized authentication architecture for vehicular networks,” IEEE Trans. Intell. Transp. Syst., early access, Sep. 24, 2020, doi: 10.1109/tits.2020.3024000.
X. Liu, H. Song, and A. Liu, “Intelligent UAVs trajectory optimization from space-time for data collection in social networks,” IEEE Trans. Netw. Sci. Eng., early access, Aug. 19, 2020, doi: 10.1109/tnse.2020.3017556.
S. Misra and S. Bera, “Soft-VAN: Mobility-aware task offloading in software-defined vehicular network,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 2071–2078, Feb. 2020, doi: 10.1109/TVT.2019.2958740.
V. H. Hoang, T. M. Ho, and L. B. Le, “Mobility-aware computation offloading in MEC-based vehicular wireless networks,” IEEE Commun. Lett., vol. 24, no. 2, pp. 466–469, Feb. 2020, doi: 10.1109/LCOMM.2019.2956514.
Shen, L.; Liu, R.; Yao, Z.; Wu, W.; Yang, H. Development of dynamic platoon dispersion models for predictive traffic signal control. IEEE Trans. Intell. Transp. Syst. 2018, 20, 431–440. [CrossRef]
Fu, R.; Zhang, Z.; Li, L. Using LSTM and GRU neural network methods for traffic flow prediction. In Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC),Wuhan, China, 11–13 November 2016; pp. 324–328.
Sharma, A.; Kumar, R. A framework for pre-computated multi-constrained quickest QoS path algorithm. J. Telecommun. Electron. Comput. Eng. JTEC 2017, 9, 73–77.
Limbasiya, T.; Soni, M.; Mishra, S.K. Advanced Formal Authentication Protocol Using Smart Cards for Network Applicants. Comput. Electr. Eng. 2018, 66, 50–63.
Dhiman, G.; Soni, M.; Slowik, A.; Kaur, H. A Novel Hybrid Evolutionary Algorithm based on Hypervolume Indicator and Reference Vector Adaptation Strategies for Many-Objective Optimization. Eng. Comput. 2020. [CrossRef]
Nair, R.; Vishwakarma, S.; Soni, M.; Patel, T.; Joshi, S. Detection of COVID-19 cases through X-ray images using hybrid deep neural network. World J. Eng. 2021. [CrossRef]
Soni, M.; Gomathi, S.; Adhyaru, B.K.Y. Natural Language Processing for the Job Portal Enhancement. In Proceedings of the 2020 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 23–24 July 2020; pp. 1–4.
Chakri, A.; Ragueb, H.; Yang, X.S. Bat Algorithm and Directional Bat Algorithm with Case Studies. In Nature-Inspired Algorithms and Applied Optimization; Springer: Cham, Germany, 2018; pp. 189–216.
Srivastava, S.; Sahana, S.K. Application of Bat Algorithm for Transport Network Design Problem. Appl. Comput. Intell. Soft Comput. 2019, 2019, 1–12. [CrossRef]
Srivastava, S.; Sahana, S.K. Nested hybrid evolutionary model for traffic signal optimization. Appl. Intell. 2016, 46, 113–123. [CrossRef]
Yao, Z.; Jiang, Y.; Zhao, B.; Luo, X.; Peng, B. A dynamic optimization method for adaptive signal control in a connected vehicle environment. J. Intell. Transp. Syst. 2020, 24, 184–200. [CrossRef]
Balta, M.; Özçelik, ˙I. A 3-stage fuzzy-decision tree model for traffic signal optimization in urban city via a SDN based VANET architecture. Future Gener. Comput. Syst. 2020, 104, 142–158. [CrossRef]
Xia, Z.; Wu, J.; Wu, L.; Chen, Y.; Yang, J.; Yu, P.S. A Comprehensive Survey of the Key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks. ACM Trans. Intell. Syst. Technol. 2021, 12, 37. [CrossRef]
Elfar, Amr, Alireza Talebpour, and Hani S. Mahmassani. "Machine learning approach to short-term traffic congestion prediction in a connected environment." Transportation Research Record 2672.45 (2018): 185-195.
Xin, Wuping, John Hourdos, and Panos G. Michalopoulos. Vehicle trajectory collection and processing methodology and its implementation. No. 08-2173. 2008.
Sun, Shuming, Juan Chen, and Jian Sun. "Traffic congestion prediction based on GPS trajectory data." International Journal of Distributed Sensor Networks 15.5 (2019): 1550147719847440.
Hosseinzadeh, Arman, and Reza Safabakhsh. "Learning vehicle motion patterns based on environment model and vehicle trajectories." 2014 Iranian Conference on Intelligent Systems (ICIS). IEEE, 2014.
Lanka, Swathi, and Sanjay Kumar Jena. "Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning." Advanced Computing, Networking and Informatics-Volume 2. Springer, Cham, 2014. 11-18.
Soldani, D., & Manzalini, A. (2015). Horizon 2020 and beyond: On the 5G operating system for a true digital society. IEEE Vehicular Technology Magazine, 10(1), 32-42.
Shah, S. A. A., Ahmed, E., Imran, M., & Zeadally, S. (2018). 5G for vehicular communications. IEEE Communications Magazine, 56(1), 111-117.
Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., ... & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work!. IEEE access, 1, 335-349.
Zhang, R., Zhong, Z., Zhao, J., Li, B., & Wang, K. (2016). Channel measurement and packet-level modeling for V2I spatial multiplexing uplinks using massive MIMO. IEEE Transactions on Vehicular Technology, 65(10), 7831-7843.
Sharma, A.; Kumar, R. A constrained framework for context-aware remote E-healthcare (CARE) services. Trans. Emerg. Telecommun. Technol. 2019, e3649. [CrossRef]
Sharma, A.; Tomar, R.; Chilamkurti, N.; Kim, B.G. Blockchain based smart contracts for internet of medical things in e-healthcare. Electronics 2020, 9, 1609. [CrossRef]
Sharma, A.; Kumar, R. Computation of the reliable and quickest data path for healthcare services by using service-level agreements and energy constraints. Arab. J. Sci. Eng. 2019, 44, 9087–9104. [CrossRef]
Geyer, F.; Carle, G. Learning and generating distributed routing protocols using graph-based deep learning. In Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Budapest, Hungary, 20 August 2018; pp. 40–45.
X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” Nature Inspired Cooperative Strategies for Optimization, vol. 284, pp. 65–74, 2010.
Reis, J.; Rocha, M.; Phan, T.K.; Griffin, D.; Le, F.; Rio, M. Deep Neural Networks for Network Routing. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019; pp. 1–8.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
