A Framework of Smart Traffic Light Control System Using Machine Learning, Simulation Modeling and Dynamic Traffic Intervals

Abstract

Intersections are a common source of delays, requiring vehicles and pedestrians to stop at red traffic signals. In recent years, researchers have proposed Smart Traffic Light Control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. In addition, these Intelligent traffic light systems tend to adapt traffic signal timings to vehicle speed, type, and the number of waiting vehicles. Very little work tries to adjust the traffic light timing based on the combined vehicle and pedestrian data. This research proposes a Smart Traffic Light Control system utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestrian and vehicle information. Also, machine learning algorithms, including Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), were demonstrated to predict traffic conditions and traffic light timings. To validate this technique, the Simulation of Urban Mobility (SUMO) platform was used to simulate the real-world intersection working. The simulation result indicates the dynamic traffic interval technique was more efficient and showcases a 12% to 27% reduction in the waiting time of vehicles and a 9% to 23% reduction in the waiting time of pedestrians at an intersection when compared to the fixed time and semi-dynamic traffic light methods. Future work includes incorporating more parameters like vehicle type and size to predict traffic conditions better. Also, pedestrians and vehicle speed, buses, and motorbikes will be considered to define the traffic light timing for detected traffic conditions.