• M. Satya Vijaya
• Chinta.TriJyotshna
• Vaka.Joshitha Ramani
• B. Bhavya
• M. Pooja Rani

In the last couple of years, an intelligent transportation system has turned into an important notion due to increased demands in road safety and efficiency of a highly linked road network. As an intrinsic part of ITS, traffic prediction can support many applications, such as road routing and traffic congestion control. First of all, let us present the ITS applications to give a more comprehensive overview of the role of traffic forecasting in ITS systems and to show how the former can improve the performance of the latter. This survey will focus on two types of prediction methods: statistics-based and machine learning–based. In general, these two types of approaches are more widely used in ITS traffic flow predictions in recent years and serve different contexts. Accordingly, we will introduce the characteristics of these two kinds of methods through concrete examples of state-of-the-art approaches. Last but not least, some potential and meaningful development directions corresponding to this domain are introduced to do a great favor for future research.The traffic environment involves everything that can affect the flow of traffic on the road, such as traffic lights, accidents, and even road repairs that might cause bottlenecks in the flow of traffic.

ITS, Traffic flow prediction, Machine Learning, Intelligent transportation system, Bottlenecks

"AI DRIVEN VEHICLE TRAFFIC FLOW PREDICTION METHODS FOR INTELLIGENT TRANSPORTATION SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.74-81, October-2024, Available :http://www.jetir.org/papers/JETIRGN06009.pdf

"AI DRIVEN VEHICLE TRAFFIC FLOW PREDICTION METHODS FOR INTELLIGENT TRANSPORTATION SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp74-81, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06009.pdf