Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

Abstract

Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.