Big Data Analytics for Traffic Flow Prediction

Abstract

Traffic jam is currently one of the most critical issues in contemporary cities because of the high rates of population growth, the possession of vehicles, and the insufficient development of the road system. Smart transportation systems (ITS) heavily rely on predicting traffic flow in the future to allow for proactive traffic control, reduce traffic congestion, optimize routes, and ensure increased safety of commuters. With the development of big data analytics, the prediction of traffic flows has been changed greatly since it taps into large amounts of heterogeneous data produced by sensors, GPS, mobile phones, social media, and intelligent vehicles. The paper is a detailed research of how big data analytics have been used to predict traffic flow. It analyses sources of data, analytics, machine learning and deep- learning models and scalable processing frameworks in modern traffic prediction systems. The most popular traditional statistical models, state-of-the-art deep learning methods convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and graph neural networks (GNN) were mentioned through an extensive literature survey. The suggested approach incorporates data preparation, feature detection, model training and performance analysis in a big data ecosystem. It has been experimentally shown that advanced analytics can be effectively implemented to enhance the accuracy of predictions and make them robust. The paper is summarized by a discussion on challenges, limitations as well as future research directions in the prediction of traffic flow using big data.