Adaptive SDN-Assisted Traffic Signal Control for Road Traffic Congestion Mitigation in VANETs

Every day, millions of drivers navigate city streets, often finding themselves stuck in long lines at intersections where traffic lights switch on a rigid, unchanging schedule. These fixed timers do not know if a lane is packed with cars or empty, leading to wasted time, extra fuel burned while idling, and a frustrating buildup of vehicles that slows down the entire network. In recent years, engineers have begun connecting cars to the road infrastructure itself, allowing vehicles to broadcast their speed and location to roadside units. This flow of real-time data offers a chance to move beyond static timers, but managing this information requires a central brain that can make quick, fair decisions for the whole system. The challenge lies in creating a control system that can instantly see which road is most crowded, give that lane the green light, and still ensure that no other direction is left waiting forever.

Researchers at Pondicherry University have developed a new approach to solve this problem by combining vehicle communication with a centralized network management system known as software-defined networking. Instead of relying on pre-set timers, their system acts as a dynamic traffic manager that constantly listens to the cars. As vehicles approach an intersection, they send out brief digital beacons containing their position and speed. Roadside units collect this information and summarize the conditions of each lane, counting how many cars are stopped or moving slowly and calculating their average speed. This summary is sent to a central controller, which acts like a traffic command center, analyzing the data to determine which lane has the heaviest demand at that exact moment.

The system then makes a decision on which traffic light should turn green. It identifies the lane with the most congestion and grants it priority, allowing those vehicles to clear the intersection first. To ensure fairness, the system uses a structured rotation method for the remaining lanes, so that if two roads have similar levels of traffic, they take turns receiving the green light rather than one being ignored. This process repeats every fifty seconds, allowing the traffic signals to adapt continuously to the changing flow of vehicles. The researchers tested this idea using a sophisticated computer simulation that modeled a four-way urban intersection with up to 250 vehicles moving through it. They compared their adaptive system against traditional fixed-time signals and other existing methods to see how well it performed under heavy traffic conditions.

The results of these simulations showed a clear advantage for the adaptive approach. When the traffic volume increased, the traditional system struggled, with vehicles waiting for over five minutes on average during peak loads. In contrast, the new system kept average wait times significantly lower, holding them to about 51 seconds even when the roads were most crowded. This efficiency translated directly into better movement of vehicles; over the course of the simulation, the adaptive system allowed 415 cars to pass through the intersection, compared to only 261 with the traditional fixed-time signals. This represents a substantial improvement in how quickly the intersection could clear the backlog of cars.

Beyond just moving cars faster, the system also improved the communication between vehicles and the road. Because the adaptive signals cleared queues more efficiently, there was less congestion on the roads, which in turn reduced the number of data packets lost or delayed during transmission. The system maintained a higher rate of successful data delivery and kept the time it took for information to travel from one point to another much lower than the conventional methods. Additionally, the reduction in idling and stop-and-go driving led to a measurable drop in fuel consumption, with vehicles using significantly less fuel per trip compared to those stuck in the longer queues created by fixed timers.

The study demonstrates that by using real-time data to guide traffic signals, cities can manage congestion more effectively without needing to build new roads. While the results come from a computer simulation rather than a physical city, the findings suggest that a system capable of listening to the road and adjusting in real-time can significantly reduce delays, save fuel, and keep communication networks running smoothly. The researchers conclude that this method offers a balanced and scalable way to handle the complex demands of modern urban traffic, proving that a flexible, data-driven approach is far superior to the rigid schedules of the past.