Issue |
ITM Web Conf.
Volume 63, 2024
1st International Conference on Advances in Machine Intelligence, and Cybersecurity Technologies (AMICT2023)
|
|
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Article Number | 01023 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/itmconf/20246301023 | |
Published online | 13 February 2024 |
A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning
Optimisation and Visual Analytics Research Lab, Faculty of Computing and Informatics, Universiti Malaysia Sabah Labuan International Campus,
87000,
Labuan,
Malaysia
* Corresponding author: jackelchew93@ums.edu.my
Vehicular traffic flow anomaly detection is crucial for traffic management, public safety, and transportation efficiency. It assists experts in responding promptly to abnormal traffic conditions and making decisions to improve the traffic flow. This survey paper offers an overview of the application of machine learning to detect anomalies in the traffic flow. Through an extensive review of the literature from the Scopus database, this paper explores the technical aspects of traffic flow anomaly detection using machine learning, including data sources, data processing approaches, machine learning algorithms, and evaluation metrics. Additionally, the paper highlights the emerging research opportunities for researchers in enhancing traffic flow anomaly detection using machine learning.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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