Intelligent technique for traffic congestion prediction in Internet of Vehicles using Randomized Machine Learning

Traffic congestion is a challenging issue faced by people and government traffic agencies. Traffic congestion not only increases travel time but also increases noise pollution, air pollution, and financial losses. There are many factors which affect the speed of a vehicle. Some of the factors are weather, wind speed, road conditions, and construction work. On highways, the low speed of vehicles can cause traffic congestion or delays. Machine learning can play a vital role in the detection of traffic congestion and hence in avoiding delays. When accurate parameters and correct structure are fed to the machine learning model, traffic congestion can be predicted accurately. This paper designs a technique to predict traffic congestion states with the help of the Extra Tree Classifier machine learning model. The proposed Extremely Randomized Machine Learning (ERML) system model predicts 94% accuracy for congestion state classification. It gives better results as compared to other machine learning models.