[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118037-en":3,"doc-seo-118037-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118037,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning - Technical Overview","Vehicular traffic flow anomaly detection plays a key role in traffic management, public safety, and transportation efficiency by enabling timely response to abnormal conditions and supporting decisions that improve traffic flow. This survey presents a literature-based overview of how machine learning is applied for traffic anomaly detection, covering data sources, data processing methods, learning algorithms, and evaluation metrics. It further identifies emerging research opportunities to strengthen anomaly detection technologies and guidance for future technical development.","A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning  \nJackel Vui Lung Chew1* and Mohammad Fadhli Asli 1  \n1Optimisation and Visual Analytics Research Lab, Faculty of Computing and Informatics, Universiti Malaysia Sabah Labuan International Campus, 87000, Labuan, Malaysia  \nAbstract. 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.  \nThrough 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.  \n1 Introduction  \nVehicular traffic flow anomaly is an unusual behaviour observed in the flow of vehicles on the road or in a traffic system. This anomaly can be referred to as phantom traffic jams in which traffic congestion and slowdowns occur on the road for no apparent reason. This anomaly is not associated with collisions, obstructions, or lane closures [1] . Traffic flow anomalies are persistent challenges to the traffic management authorities. For instance, managing traffic flow requires costly technologies to implement and maintain [2,3] . Then, congestion in traffic causes economic losses due to wasted fuel and time delay [4,5] . In addition, congested traffic increases greenhouse gas emissions, contributing to air pollution and climate change [6,7] . Moreover, irregular traffic flow increases the likelihood of traffic accidents [8-10] .  \nDetecting anomalies in traffic flow is essential for appropriate responses to traffic incidents and optimising traffic management approaches. Machine learning (ML) offers a promising solution to tackle traffic flow anomaly detection challenges. For example, ML can analyse historical traffic data to predict various traffic conditions. These predictions can help traffic management authorities make informed routing to optimise traffic flow [11,12] . Predictions made by ML can also help reduce economic losses by reducing congestion and optimising traffic flow [13] . Furthermore, ML can be utilised to develop analytical models for classifying accident-prone locations and times. These models allow relevant authorities to implement appropriate safety measures [14] proactively.  \n* Corresponding author: [jackelchew93@ums.edu.my](jackelchew93@ums.edu.my)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nSeveral authors have provided reviews or surveys on the application of ML in vehicular traffic flow. However, it was found that limited literature mainly focuses on detecting traffic flow anomalies. To the best of our search of existing surveys and reviews about traffic flow anomaly detection using ML, two published survey and review papers partially like our paper [15,16] . The limitations of these papers have been identified and can be explained as follows. The former surveyed ML in optimising operations in freight transportation, supply chain, and logistics management. A brief review of anomaly detection on transportation data using ML is provided. However, the review of the technical aspect of ML to detect anomalies in transportation data is limited. The latter comprehensively discussed ML methods used in intelligence transportation systems applications. However, the literature gap in the technical aspect of M","cbCaio7Deb5U5Dwh","https://ap.wps.com/l/cbCaio7Deb5U5Dwh","pdf",294393,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature Review and Related Work\n## Survey Scope and Motivation\n## Differences From Existing Surveys\n# Survey Methodology\n## PRISMA-Guided Selection Criteria\n## Scopus Search Strategy and Screening\n# Organization of the Paper","[{\"question\":\"Why is vehicular traffic flow anomaly detection important?\",\"answer\":\"It helps traffic management authorities respond promptly to abnormal traffic conditions and make better decisions to improve traffic flow, supporting public safety and transportation efficiency.\"},{\"question\":\"What aspects does the survey cover regarding machine learning for anomaly detection?\",\"answer\":\"The survey reviews data sources, data processing approaches, machine learning algorithms, and evaluation metrics used for detecting anomalies in traffic flow.\"},{\"question\":\"How does the survey select and screen relevant papers?\",\"answer\":\"It follows PRISMA guidelines, defines inclusion criteria focused on published English literature from 2019 onward, searches Scopus with specific query terms, excludes non-relevant and non-technical review items, and further expands findings via inner references.\"}]","A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning - 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