A Research Survey on Real-Time Intelligent Traffic System

Abstract

Nowadays, it is crucial to continuously monitor and provide real-time analysis to reduce traffic-related accidents and practices such as vehicle overloading on the roads daily. As a result, we reviewed the literature's numerous methods and techniques for vehicle detection, recognition, identification, speed estimation, and license plate recognition. In this analysis, we examined 42 articles published in the last ten years, from 2012 to 2022. Based on our research, we found that the Deep CNN is the optimum method for vehicle categorization. The motivation of this review is that none of the aforementioned models are combined into a single model, so we present a comprehensive list of all these models that may be helpful to anyone conducting the study in this area. Therefore, after reviewing the chosen research publications, we propose 20+ datasets that might be used in the field for more research. We also discovered 15+ different ML models used to detect and identify vehicles. Finally, we observed that combining machine learning and AI (Artificial Intelligence) to create intelligent traffic control systems is a promising research area.