Traffic management systems: a survey of current solutions and emerging technologies

With the rapid increase in the number of vehicles on the road, the necessity for traffic management systems became apparent. In order to effectively control traffic, a number of technical solutions were proposed to solve traffic issues, such as eliminating traffic congestion and identifying the shortest routes. Researchers are motivated to use various data-driven solutions that assist decision-makers in making timely decisions due to the enormous amount of traffic data that has been gathered by utilizing sensors, traffic signals, and cameras. The primary objective of this research is to analyze the current traffic management systems from several technical perspectives. According to the technology employed, this study reviews recent traffic management system methodologies and classifies them into five main groups: machine learning-based, fuzzy logic-based, statistically-based, graph-based, and hybrid approaches. Each group is presented together with a thorough overview of its scope, main challenges, analysis type, and dataset. Researchers and practitioners are anticipated to use this study as a guide to develop new technical-based traffic management systems, as well as to propose new contributions or enhance current ones.