Machine Learning-Based Traffic Prediction for Intelligent Transportation Systems
Abstract
The goal of this study is to provide a mechanism for forecasting precise and timely traffic flow data. Traffic Environment refers to anything that could have an impact on how much traffic is moving along the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an educated choice if they have previous knowledge that is very close to approximation about all the above and many more real-world circumstances that can impact traffic. Additionally, it aids in the development of driverless cars. Traffic data have been growing dramatically in the recent decades, and we are moving toward big data concepts for transportation. The current approaches for predicting traffic flow employ certain traffic prediction models, however they are still insufficient to deal with real-world situations.
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