A Review on Traffic Forecast Based on Machine Learning and Deep Learning Models

From the previous discussion, it is evident that smart and intelligent traffic systems are essential for effectively managing the high traffic volumes in urban and semi-urban areas worldwide, which are typically caused by major population shifts. Although there are several different uses for smart traffic monitoring, including optimizing routes and navigation, managing parking, controlling lighting, detecting accidents, identifying road anomalies, and managing infrastructure, it is crucial to consider the calculation of traffic volume across the entire network. This study provides a comprehensive analysis of contemporary statistical methods used to forecast the volume of traffic throughout a whole network. It focuses on important features of previous research in this area. The objective is to lay the groundwork for creating efficient and precise prediction models for traffic volumes in the realm of intelligent traffic and transportation systems.

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