Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow

The digital revolution births transformation in many facets of today’s society. Its adoption in transportation to curb traffic congestion in major cities globally advances smart-city initiatives. Challenges of population growth, lack of datasets, and aging infrastructure have necessitated the need for traffic analytics. Studies have estimated an associated global annual loss of $583 billion to traffic congestion for 2023. This, caused fuel wastage, loss of time, and increased costs across congested areas. With the cost of building more road networks, cities must advance new ways to improve traffic flow via anomaly detection as an early warning in the flow pattern. Our study posits stacked learning with extreme gradient boost as a meta-learner to help address imbalanced datasets, yield faster model construction, and ensure improved performance via enhanced anomalous data detection.

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Publication Details

Journal
Journal of Fuzzy Systems and Control
Published
2024-10-21
DOI
https://doi.org/10.59247/jfsc.v2i3.267
Citations
4
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.40
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article

Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow

Victor Ochuko Geteloma, De Rosal Ignatius Moses Setiadi, Andrew Okonji Eboka, Arnold Adimabua Ojugo et al.
4 citations
Journal of Fuzzy Systems and Control
Network Security and Intrusion Detection
1.40
article

Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow

Victor Ochuko Geteloma, De Rosal Ignatius Moses Setiadi, Andrew Okonji Eboka, Arnold Adimabua Ojugo, Patrick Ogholuwarami Ejeh, Christopher Chukwufunaya Odiakaose, Rita Erhovwo Ako, Margaret Dumebi Okpor, Amaka Patience Binitie, Tabitha Chukwudi Aghaunor, Anderson Afotanwo
article en
4 citations

Abstract

The digital revolution births transformation in many facets of today’s society. Its adoption in transportation to curb traffic congestion in major cities globally advances smart-city initiatives. Challenges of population growth, lack of datasets, and aging infrastructure have necessitated the need for traffic analytics. Studies have estimated an associated global annual loss of $583 billion to traffic congestion for 2023. This, caused fuel wastage, loss of time, and increased costs across congested areas. With the cost of building more road networks, cities must advance new ways to improve traffic flow via anomaly detection as an early warning in the flow pattern. Our study posits stacked learning with extreme gradient boost as a meta-learner to help address imbalanced datasets, yield faster model construction, and ensure improved performance via enhanced anomalous data detection.

Journal of Fuzzy Systems and ControlVol. 2(3)
University of Science and Technology (YE), Robert Morris University (US), Federal College of Education, Kano (NG), The Federal Polytechnic, Ado-Ekiti (NG), Universitas Dian Nuswantoro (ID), Dennis Osadebay University, Federal University of Petroleum Resource Effurun (NG)
Openalex Percentile: Top 16%
Network Security and Intrusion Detection
1.40
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