Comprehensive analysis of UK AADF traffic dataset set within four geographical regions of England

Abstract

Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset ‘estimated annual average daily flows (AADFs) Data—major and minor roads’ from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study.

Publication DOI: https://doi.org/10.1111/exsy.13415
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
Additional Information: This work is partly supported by VC Research (VCR 0000186) for Prof. Chang. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited. Copyright © 2023 The Authors.
Uncontrolled Keywords: Random Forest,algorithms for traffic analysis,machine-learning algorithms,traffic analysis,traffic flow,Artificial Intelligence,Theoretical Computer Science,Control and Systems Engineering,Computational Theory and Mathematics
Publication ISSN: 1468-0394
Last Modified: 17 May 2024 07:19
Date Deposited: 22 Aug 2023 16:24
Full Text Link:
Related URLs: https://onlinel ... 1111/exsy.13415 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-12
Published Online Date: 2023-08-19
Accepted Date: 2023-07-19
Submitted Date: 2023-04-09
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Xu, Qianwen Ariel (ORCID Profile 0000-0003-0360-7193)
Hall, Karl
Oluwaseyi, Olojede Theophilus
Luo, Jiabin (ORCID Profile 0000-0002-2599-2822)

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