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

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.

Automated summary

Traffic flow detection is crucial in freeway traffic surveillance systems. Despite significant investment in monitoring and analyzing traffic congestion, autonomous traffic analysis remains challenging due to the complexity of traffic delays. This study presents an intelligent analytic method based on machine-learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom, achieving high accuracy and demonstrating the practical insights in traffic analysis.