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Article

Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing

by , and *
Department of Computer Engineering, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Juan Francisco De Paz Santana
Appl. Sci. 2021, 11(13), 5861; https://doi.org/10.3390/app11135861
Received: 24 May 2021 / Revised: 9 June 2021 / Accepted: 23 June 2021 / Published: 24 June 2021
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively. View Full-Text
Keywords: Ambient Intelligence; dynamic graph embedding; vehicular edge computing; incident detection; Internet of Things Ambient Intelligence; dynamic graph embedding; vehicular edge computing; incident detection; Internet of Things
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MDPI and ACS Style

Li, G.; Nguyen, T.-H.; Jung, J.J. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing. Appl. Sci. 2021, 11, 5861. https://doi.org/10.3390/app11135861

AMA Style

Li G, Nguyen T-H, Jung JJ. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing. Applied Sciences. 2021; 11(13):5861. https://doi.org/10.3390/app11135861

Chicago/Turabian Style

Li, Gen, Tri-Hai Nguyen, and Jason J. Jung. 2021. "Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing" Applied Sciences 11, no. 13: 5861. https://doi.org/10.3390/app11135861

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