Authors: K.Ganesh, Assistant Professor K.Abhiram
Abstract: With these new tools, we can only speculate as to the level of intelligence that the next generation will achieve by modifying current methods. More and more, society benefits from increasingly sophisticated computing techniques. This article explores and discusses the smart road traffic system, which is one of the advantages. Using these state-of-the-art computational approaches will undoubtedly make the control system for road traffic lights easier and more efficient in the future. In order to optimize the overall trip time, the road traffic system takes into account several parameters, such as total travel time, wait time or delay time in the traffic line, vehicle speed, etc. There are a few issues with the road traffic flow, such as drivers not obeying the regulations of the road, accidents, and congestion. The real issue with traffic congestion is the lack of a better model for managing traffic on roads, which necessitates the dedication of researchers to finding a solution. The current traffic light control system has a few limitations, such as not being able to adjust the delay time based on the density of traffic, not being able to prioritize lane flow when emergency vehicles like fire trucks and ambulances are on the road, and so on. These studies aim to find solutions to the aforementioned traffic issues and, using the aforementioned factors, to optimize the overall travel time in a way that causes minimal delay or congestion. To address these issues, this study employs a probabilistic traffic queue analytical model to find a computationally efficient traffic model. An improved traffic light management system is developed by simulating a model that takes vehicle categorization into account and tests it with various traffic conditions. The model accounts for a variable time delay.
