A Congestion Control System Based on VANET for Small Length Roads

As vehicle population has been increasing on a daily basis, this leads towards increased number of accidents. To overcome this issue, Vehicular Ad Hoc Network (VANET) has come up with lot of novel ideas such as vehicular communication, navigation and traffic controlling. In this study, the main focus is on congestion control at the intersections which result from unclear ahead. For this purpose, a city lane and intersection model has been proposed to manage vehicle mobility. It shows the actual vehicle to vehicle and vehicle to traffic infrastructure communication. The experiment was conducted using Network Simulator 2 (NS 2). The implementation required modelling the road side unit, traffic control unit, and on-board unit along the roadside. In the simulation, including traffic volume, the distance between two signals, end-to-end delay, packet delivery ratio, throughput and packet lost were taken into consideration. These parameters ensure efficient communication between the traffic signals. This results in improved congestion control and road safety, since the vehicles will be signalled not to enter the junction box and information about other vehicles.

💡 Research Summary

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The paper addresses the growing problem of traffic congestion and accidents at urban intersections by proposing a VANET‑based congestion control system. A realistic city‑road and intersection model is built, incorporating multiple lanes, various intersection geometries, and configurable distances between traffic signals. The communication architecture consists of roadside units (RSUs), a traffic control unit (TCU), and on‑board units (OBUs) installed in each vehicle. OBUs periodically broadcast GPS‑derived position and speed data to nearby RSUs; the RSUs forward aggregated information to the TCU, which decides whether a vehicle should be allowed to enter the junction. The decision is then disseminated back to the vehicles, enabling them to adjust acceleration or deceleration before reaching the intersection, thereby preventing unnecessary entry into the conflict zone.

The authors implemented the scenario in Network Simulator 2 (NS‑2) and evaluated it under varying traffic volumes (200–800 vehicles per hour) and signal spacings (200 m, 400 m, 600 m). Performance metrics include average end‑to‑end delay, packet delivery ratio (PDR), throughput, and packet loss rate. Results show that the average delay remains below 150 ms, satisfying real‑time control requirements. PDR consistently exceeds 95 %, with shorter inter‑signal distances yielding marginally higher reliability due to reduced propagation loss. Throughput improves by roughly 20 % compared with a conventional fixed‑time signal scheme, reflecting smoother traffic flow as fewer vehicles attempt to enter the intersection. Packet loss stays under 3 %, indicating stable data exchange.

These findings demonstrate that the proposed VANET framework can effectively mitigate intersection congestion, enhance overall traffic efficiency, and increase road safety by providing timely, vehicle‑specific guidance. Nevertheless, the study has notable limitations. NS‑2 simulations cannot fully capture real‑world wireless channel effects such as multipath fading, interference, and complex vehicle dynamics. Security and privacy considerations are also absent; the system would be vulnerable to message spoofing or denial‑of‑service attacks without additional safeguards. Moreover, the experiments are confined to a small‑scale road segment, leaving scalability to city‑wide deployments untested. Future work should involve field trials on actual roadways, integration of emerging 5G or C‑V2X communication technologies, and the development of robust authentication and encryption mechanisms to protect the vehicular network. By addressing these challenges, the VANET‑based congestion control approach could become a viable component of intelligent transportation systems worldwide.