Traffic Prediction for Intelligent Transportation System using Machine Learning

Nowadays, the large number of vehicles has caused a serious traffic congestion problem in modern city and deeply affects our daily life. Traffic congestion results in low throughput, excess delays, less safety insurance, and so on. The growing number of vehicles on streets increased the air pollution that emits greenhouse gases such as carbon dioxide. Furthermore, idling vehicles caused by traffic jams will waste more fuel and produce more pollution. All these situations remind us the importance of traffic management, with which to optimize the traffic stream and public traffic choices of citizens in smart cities. 

This project aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyze the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles.

Keywords: Traffic Environment, Deep Learning, Machine Learning, Genetic Algorithms, Soft Computing, Big Data, Image Processing.