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Graph-Based Machine Learning for Enhanced Traffic Management: Optimizing Efficiency and Mobility
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Author(s): S. Sriram (Department of Computing Technologies, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)and R. Krishna Kumari (Department of Mathematics, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)
Copyright: 2025
Pages: 28
Source title:
Practical Applications of Machine Learning and AI: Medicine, Environmental Science, Transportation, and Education
Source Author(s)/Editor(s): Toufik Mzili (Chouaib Doukkali University, Morocco)and Adarsh Kumar Arya (Harcourt Butler Technical University, India)
DOI: 10.4018/979-8-3373-1399-3.ch009
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Abstract
The urban population's rapid growth has heightened traffic congestion challenges, necessitating innovative solutions. This paper explores the potential of Graph-Based Machine Learning (GBML) in transforming traffic management. It focuses on enhancing efficiency to reduce travel times and congestion, and improving mobility for a better user experience and urban accessibility. We assess the effectiveness of logistic regression and random forest for classifying traffic situations. While logistic regression offers interpretability, random forest outperforms it in accuracy. Random forest excels in capturing complex traffic data relationships, aiding tasks like traffic flow prediction and anomaly detection. Its adaptability to diverse datasets ensures robust performance across various traffic scenarios. Therefore, random forest emerges as the more valuable methodology in traffic analysis due to its superior accuracy, robustness, and adaptability to complex traffic patterns.
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