Machine Learning-Based Traffic Prediction for Intelligent Transportation Systems

Abstract

The goal of this study is to create a mechanism for forecasting precise and timely traffic flow data. Traffic Environment refers to everything that might have an impact on how much traffic is moving down the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an informed choice if they have previous knowledge that is very close to approximate about all the above and many more real-world circumstances that can affect traffic. Additionally, it aids in the development of driverless vehicles. Traffic data have been growing dramatically in the recent decades, and we are moving towards big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still insufficient to deal with real-world situations.