Automatic Prediction of Real time Traffic for Intelligent Transport System Using ML Algorithms

Abstract

In current days there is a great demand for traffic management in terms of predicting accurate and timely traffic flow. As we all know a lot of survey reports tell that by maintaining proper transport system using intelligent decisions, one can able to reduce the traffic jam on roads. A lot of primitive works have been undergone to solve this problem but no method proved to be successful in predicting traffic on roads very accurately and efficiently. This motivated me to design the proposed work for predicting the real-time traffic for intelligent transport systems by using machine learning algorithms. In general, the traffic environment can affect the traffic flowing on the road due to the following reasons such as traffic signals, accidents, rallies, roads repairs, seminars, functions, weather problems, and a lot more. If the users who drive that way have some prior information about that traffic, the rider can choose an alternate router and can avoid that traffic problem. As there is a huge increase in population as well as the usage of vehicles also increased a lot, which is the main reason for getting traffic problems in the transport system. A lot of big data concepts for transportation management is developed till now but no method is accurate in finding the traffic flow accurately and plan effectively. Hence this motivated me to design the prediction of real-time traffic using ML algorithms combined with genetic and deep learning algorithms to analyze the traffic-related problems more efficiently. For the experimental purposes here we collect some sample traffic-related images and then try to apply image processing algorithms for finding the traffic signs and predict the alternate solution for those traffic problems.