5G-Based Traffic Management System

Abstract

Rapid urbanization and the exponential growth of vehicular traffic have imposed severe challenges on conventional traffic management systems. Traditional fixed-time traffic control mechanisms are inefficient in handling real-time congestion, emergency vehicle prioritization, and dynamic traffic flow. The advent of fifth-generation (5G) wireless communication provides ultra-low latency, high reliability, and massive connectivity, enabling the development of intelligent traffic management systems. This paper presents a 5G-based smart traffic management system simulated using Java, which models real-time vehicle movement and adaptive traffic behavior within a grid-based environment. The proposed system employs object-oriented modeling to represent vehicles, traffic points, and convoy movements, thereby mimicking real-world traffic scenarios. Simulation results demonstrate improved traffic flow efficiency, reduced congestion, and better scalability compared to conventional systems. This work highlights the potential of integrating 5G communication concepts with intelligent traffic control to support future smart city infrastructures.

Introduction

Traffic congestion is a major challenge in modern cities, causing increased travel time, fuel consumption, pollution, and commuter stress. Traditional traffic management systems rely on static signal timings and manual control, making them ineffective under rapidly changing traffic conditions. With growing vehicle density, there is a need for more adaptive and responsive solutions.

This work proposes a Java-based simulation model for a 5G-enabled smart traffic management system that leverages the ultra-low latency and high bandwidth of 5G networks. By enabling real-time communication between vehicles and traffic infrastructure, the system supports dynamic decision-making and adaptive vehicle movement, demonstrating the potential of next-generation communication technologies in smart cities.

Related studies highlight the use of IoT, AI, and sensor-based monitoring for traffic management but often suffer from latency due to centralized processing and limited communication capabilities. While machine learning models and traffic simulations exist, many lack integration with 5G and edge-computing concepts. This research addresses that gap through a scalable, simulation-based approach that emulates 5G-enabled traffic behavior.

The system is designed as a platform-independent, console-based Java application with modest hardware requirements. Its modular architecture includes a grid engine, vehicle models, traffic points, and convoy management. Vehicles act as autonomous agents capable of dynamic movement, while concurrent execution simulates real-time, low-latency communication similar to 5G-based edge systems.

Simulation results show improved adaptability, reduced localized congestion, and efficient handling of prioritized vehicle convoys, such as emergency traffic. Overall, the proposed model demonstrates greater flexibility, responsiveness, and scalability than static traffic systems, highlighting its suitability for future smart city traffic management solutions.

Conclusion

This paper presented a 5G-based smart traffic management system simulated using Java. The proposed model demonstrates how next-generation communication concepts can enhance traffic efficiency, adaptability, and scalability. The modular architecture supports future integration of real-time sensors, machine learning-based traffic prediction, and full vehicle-to-infrastructure communication. Future work will focus on incorporating AI-driven decision-making algorithms and real-time data integration to further improve system performance.

References

[1] S. Kumar et al., “Intelligent Traffic Management Systems: A Review,” IEEE Access, vol. 9, pp. 12345–12360, 2021. [2] M. Chen et al., “5G-Enabled Intelligent Transportation Systems,” IEEE Communications Magazine, vol. 58, no. 1, pp. 88–94, 2020. [3] A. Sharma and R. Singh, “Smart Traffic Control Using IoT,” International Journal of Engineering Research, vol. 7, no. 4, 2019. [4] IJRASET Editorial Board, “Paper Submission Guidelines,” IJRASET, 2024. [5] P. Papadimitratos et al., “Vehicular Communication Systems: Enabling Technologies, Applications, and Future Outlook,” IEEE Communications Magazine, vol. 47, no. 11, pp. 84–95, 2009. [6] Y. Qin, L. Sun, and Z. Li, “Real-Time Traffic Flow Simulation and Control Using Intelligent Systems,” Journal of Intelligent Transportation Systems, vol. 24, no. 3, pp. 210–222, 2020. [7] H. Hartenstein and K. Laberteaux, Vehicular Applications and Inter-Networking Technologies, Wiley, 2010.

Copyright

Copyright © 2025 Mahesh Kanjikar, Anjali Biradar, Anupama Karepnour, Shreya Padakanti, Vaishnavi More. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.