4.6 Article

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

Journal

APPLIED SCIENCES-BASEL
Volume 11, Issue 13, Pages -

Publisher

MDPI
DOI: 10.3390/app11135861

Keywords

Ambient Intelligence; dynamic graph embedding; vehicular edge computing; incident detection; Internet of Things

Funding

  1. Chung-Ang University
  2. National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2019K1A3A1A80113259]
  3. National Research Foundation of Korea [2019K1A3A1A80113259] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study introduces a new anomaly detection method based on dynamic graph embedding for large time series datasets from the Internet of Things in Ambient Intelligence-enabled smart environments. Experimental results demonstrate significant improvements in traffic incident detection compared to other baseline methods.
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.

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