A Scalable Intelligent Traffic Signal Framework Using IoT Sensing and Predictive ML Models

Abstract

Traffic congestion in the urban setting is one of the biggest remaining challenges of modern cities. Rapid increase in population, increasing number of vehicles and insufficient development of road infrastructure as a whole has worsened congestion problems, leading to increase in travel time, high fuel consumption and high emission level and decline in overall urban living standards. Traditional fixed-time traffic signal controllers are based on predetermined schedules and are unable to adjust to real-time fluctuations in traffic flow and pedestrian behavior and unforeseen events such as accidents or road maintenance. These limitations make them less effective and contribute to inefficient traffic management. To overcome these problems, a Smart Traffic Light Control System with the integration of Internet of Things (IoT) sensor networks and advanced Machine Learning (ML) algorithms for dynamic, data-driven signal optimization is proposed in this study. The system continuously gathers and processes the real-time traffic data from the IoT devices such as Vehicle Count sensors, RFID units, surveillance cameras, environment sensors. These different data streams enable an accurate and comprehensive picture of the traffic behavior through multiple junctions to be created. ML models intend to take both the past and recent real-time traffic datasets and learn to forecast the level of congestion, approximate queue lengths and pinpoint the peak-hour travel flow patterns. Supervised learning methods (such as Random Forest, Gradient Boosting, LSTM network) are employed for traffic forecast, and the reinforcement learning algorithms are used for dynamic adjustment of signal time and phase time. This hybrid method of ML allows the system to self-adapt to ever-changing road conditions and distribute green light intervals in a more efficient way. The proposed model will strive to minimize traffic delays and increase the vehicle throughput, as well as road safety and the impact on the environment. It has a scalable architecture that enables it to interface with the current Intelligent Transportation Systems (ITS) and be extended to cover large urban networks. Experiments using simulation, backed up by actual traffic data, indicate significant betterment in average waiting time and signal responsiveness and movement of the whole traffic as compared to conventional fixed-time and actuated signal systems. These findings validate the hypothesis that the traffic signal management system based on the IoT and ML can introduce a ground-breaking solution to the optimization of urban mobility and congestion and facilitate the evolution and actualization of smarter and more sustainable cities.