In metropolitan cities traffic congestion became a severe issue due to large scale multiple-layer road networks. The multifaceted nature, heterogeneity of traffic framework and the enormous information challenge have turned out to be generous troubles. The current transportation systems deal with these issues with the requirement of qualified overall prediction accuracy. Checking, foreseeing and understanding traffic conditions in any city is a vital issue for city arranging. More recently, the development of new technology for traffic data processing using big data for accurate traffic prediction has shifted the spotlight to data-driven procedures. Different researchers build traffic forecasting systems using big data analytics in order to prevent traffic congestion and accident issues. However, most of the researchers focus on the prediction of individual road segments or intersections instead of the multilayer roads. This paper is an attempt to review the different techniques used by numerous researchers for traffic forecasting using big data analytics. The ultimate goal of this work is to set an updated compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

Traffic Forecasting system, Big Data Analytics, Smart Transport System (STS)

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