Support Vector Machines (SVM) in Traffic Prediction for Intelligent Transportation Systems- A comprehension Review

The Intelligent Transportations System has garnered interest due to rising road safety and effectiveness demands in more interconnected road networks. Traffic prediction is an integral component of ITS and may aid in numerous areas, including road routing and congestion reduction. So, many people in the research community are interested in intelligent transportation systems. Only a few review articles have addressed this topic. This paper presents a comprehensive literature review of support vector machines (SVMs), evaluation, classification, and essential concepts of recent literature sources. It also provides valuable information to transportation application researchers and developers. There is ongoing research into the potential uses of the SVM, a trustworthy and efficient classification method in machine learning. This work has addressed the utilization of the SVM classifier by reviewing several papers. This article summarizes the research on using support vector machines (SVMs) to forecast path obstacles. After a comprehensive analysis and thoughtful topic selection, the findings reveal all the unsolved problems and produce some significant discoveries that could be considered good future research directions.