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

Sami Shaffiee Haghshenas; Vittorio Astarita; Giuseppe Guido; Mohammad Hassan Mobini Seraji; Paola Andrea Aldana Gonzalez; Ahmad Haghdadi; Sina Shaffiee Haghshenas

doi:10.47852/bonviewJCCE32021062

Authors

Sami Shaffiee Haghshenas Department of Civil Engineering, University of Calabria, Italy https://orcid.org/0000-0002-9301-8677
Vittorio Astarita Department of Civil Engineering, University of Calabria, Italy
Giuseppe Guido Department of Civil Engineering, University of Calabria, Italy
Mohammad Hassan Mobini Seraji Department of Civil Engineering, University of Calabria, Italy
Paola Andrea Aldana Gonzalez Department of Civil Engineering, University of Calabria, Italy
Ahmad Haghdadi Tarhe Chaharome Arya, Consulting Engineers, Iran
Sina Shaffiee Haghshenas Department of Civil Engineering, University of Calabria, Italy https://orcid.org/0000-0003-2859-3920

DOI:

https://doi.org/10.47852/bonviewJCCE32021062

Keywords:

machine learning, traffic flow, data analysis, Web of Science (WOS)

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 (ML) 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 ML techniques in the management of traffic flow. Using the Web of Science platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using ML 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 ML 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.

Received: 11 May 2023 | Revised: 12 July 2023 | Accepted: 20 July 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.