Traffic Prediction Using a Supervised Learning Approach

摘要

With the world moving towards smart cities, one of the unprecedented problems faced is vehicular road traffic congestions. Due to population increase in large urban areas there is an increase in vehicular density, leading to traffic jams and problems such as increasing commuting time, raise in transportation cost, delayed services and increase in fuel consumption. The proposed solution attempts to address the problem of traffic congestions caused by inefficient handling of traffic. Though image processing can be used as an effective way to monitor the number of vehicles on the road, it too has certain drawbacks due to which data collected by RFID tagging is used. With the data collected from the traffic congestions over the previous few years across different types of roads all over London, the patterns in the data can be studied to predict congestion on a particular road on a particular given time.