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Unsupervised anomaly detection based method of risk evaluation for road traffic accident

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Abstract

Elevated road plays a very important role as corridors in urban traffic network, and the occurrence of traffic accidents often causes a great impact. In that sense, we propose a unique and enhanced Autoencoder (AE) to identify elevated road traffic accident (RTA) risk based on traffic anomaly detection in an unsupervised manner. An attention mechanism is introduced to extract the traffic condition features considering traffic spatiotemporal variation characteristics. Additionally, an enhanced loss is also introduced to optimize the ability of unsupervised anomaly detection (UAD) approach to detect anomalous RTA risk and persistent anomalous traffic condition, which can significantly boost the anomaly detection performance using the contaminated traffic condition datasets. To assess the RTA risk, the evaluation mechanism and discriminant threshold are used to quantitatively analyze the detected abnormal traffic condition. Finally, experiments on real traffic datasets demonstrate the effectiveness of the model.

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Acknowledgements

The authors are very grateful to the anonymous referees for their valuable comments and suggestions. This work was supported by National Natural Science Foundation of China (NSFC) under the Grant No. 71971135. And industrial and Informationalization Ministry of China for Cruise Program (No. 2018-473), and Key Project of National Social and Scientific Fund Program (18ZDA052).

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Authors and Affiliations

  1. School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China

    Chao Zhao, Xiaokun Chang & Jian Wu

  2. China Design Group Co.,Ltd., Nanjing, China

    Tian Xie

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

Authors
  1. Chao Zhao
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  2. Xiaokun Chang
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  3. Tian Xie
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  4. Hamido Fujita
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  5. Jian Wu
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Correspondence to Jian Wu.

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Zhao, C., Chang, X., Xie, T. et al. Unsupervised anomaly detection based method of risk evaluation for road traffic accident. Appl Intell 53, 369–384 (2023). https://doi.org/10.1007/s10489-022-03501-8

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  • DOI: https://doi.org/10.1007/s10489-022-03501-8

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