Improving Network Performance in Intelligent Transportation Systems Using Real Traffic Data
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Abstract
This paper focused on developing approaches to improving the intelligent transportation systems network performance by incorporating actual traffic information and optimized computation methods. The course of the investigation entails the assessment of three protocols including the vehicular ad hoc network, 5G and a blend of both at different pool traffic densities. Based on observations made in latency, throughput, and efficiency, the most suitable protocol for intelligent transportation systems implementation is identified. The study is divided into a number of steps comprising the study of network performance under various traffic loads, the absolute comparison of the protocols, and the last step which aims at the simulation of the enhanced algorithms. Analysis shows that the proposed hybrid architecture performs better than regular vehicular ad hoc network and 5G systems in high density traffic conditions. Furthermore, this study shows how real-time data can enhance system interactivity and effectiveness in core processes. The findings advance the understanding of intelligent transportation systems and shed light on future work towards the optimization of intelligent transportation systems to increasingly allow for urban mobility and automated vehicle systems. It also proposes other directions of research on the synergy of machine learning with edge computing for network protocol enhancement.
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1. H. Hartenstein and K. P. Laberteaux, VANET: Vehicular Applications and Inter-Networking Technologies. Wiley, 2010.
2. J. Choi et al., “5G technologies for smart transportation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 624–635, 2020.
3. M. Gerla, E.-K. Lee, G. Pau, and U. Lee, “Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds,” in 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014, pp. 241–246.
4. F. Li, J. Wang, and H. Wu, “Real-time data-driven traffic prediction for urban road networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 180–191, 2022.
5. X. Ma, X. Yu, and S. Wang, “Scalability challenges in ITS and potential solutions using edge computing,” IEEE Communications Magazine, vol. 58, no. 3, pp. 45–51, 2020.
6. R. Hussain et al., “Secure and efficient communication frameworks for ITS: Survey and perspectives,” IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 58–82, 2021.
7. H. Zhang, Z. Liu, and W. Li, “The role of real-time traffic data in enhancing urban traffic management,” Transportation Research Part C: Emerging Technologies, vol. 112, pp. 123–135, 2020.
8. A. Shalaby and A. Farah, “Improving traffic flow using real-time data in intelligent transportation systems,” Journal of Intelligent Transportation Systems, vol. 23, no. 4, pp. 456–467, 2019.
9. X. Yang, F. Wang, and Y. Zhang, “Real-time data for optimal traffic flow management: A review,” Transportation Science, vol. 55, no. 5, pp. 987–1004, 2021.
10. J. Li, L. Li, and X. Zhang, “Challenges and opportunities of real-time traffic data in ITS,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 346–358, 2020.
11. S. Raza and M. Tariq, “VANET and ITS: Communication systems for future transport,” IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 822–841, 2021.
12. X. Wang, Y. Zhang, and Y. Liu, “The role of 5G in intelligent transportation systems: Enhancing real-time data transmission,” IEEE Transactions on Mobile Computing, vol. 21, no. 7, pp. 1340–1352, 2022.
13. S. Zhang and K. Xie, “Edge computing in intelligent transportation systems: Reducing latency in real-time traffic data processing,” IEEE Access, vol. 8, pp. 23456–23465, 2020.
14. M. Silva and S. Tavares, “Challenges in real-time traffic data collection and analysis in urban environments,” Urban Transport and the Environment, vol. 15, pp. 108–120, 2019.
15. A. Ghosh and P. Kumar, “Standardization of traffic data in ITS: A step toward better integration,” Transportation Research Part B: Methodological, vol. 144, pp. 45–58, 2021.
16. D. Lee and Y. Kim, “Dynamic traffic signal control using real-time data: A case study,” Journal of Traffic and Transportation Engineering, vol. 17, no. 2, pp. 112–126, 2020.
17. H. Abolhasani and H. Rezaei, “Real-time incident detection in intelligent transportation systems,” International Journal of Intelligent Systems, vol. 35, pp. 1101–1115, 2020.
18. M. A. K. Akin, “Vehicular ad hoc networks (VANETs) for intelligent transportation systems,” International Journal of Computer Applications, vol. 34, no. 5, pp. 22–30, 2011.
19. T. D. Vu, S. A. H. Phan, and K. J. Kim, “Performance evaluation of 5G networks for ITS,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 3456–3467, Dec. 2017.
20. J. C. Lin, J. K. Ng, and K. L. Wang, “Real-time traffic data collection and traffic prediction with 5G networks,” in Proceedings of the IEEE International Conference on Communications, May 2019, pp. 98–105.
21. S. Wang, L. Chen, and M. Liu, “Application of machine learning in traffic management,” Transportation Research Part C: Emerging Technologies, vol. 50, pp. 213–227, 2018.
22. P. M. Barros and T. T. H. Nguyen, “Data analytics for traffic management in smart cities,” Journal of Urban Technology, vol. 25, no. 3, pp. 115–132, 2022.
23. L. S. Tan, “Integration of traffic data sources for efficient ITS,” IEEE Transactions on Vehicular Technology, vol. 63, no. 9, pp. 1021–1034, Sep. 2014.