Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Abstract

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.