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Automatic incident detection using edge-cloud collaboration based deep learning scheme for intelligent transportation systems

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Abstract

Automatic incident detection not only plays an important role in traffic safety management, but also contributes to the operation of intelligent transportation systems. Although the emerging information technologies and artificial intelligence approaches are paving the way for high-precision incident detection, existing incident detection methods fail to handle the unbalanced incident data with excessive zero observations. Also, issues related to network delays and privacy leakage of centralized computing are prevalent. To fill the above gaps, this study proposes a novel automatic incident detection paradigm using an edge-cloud collaboration mechanism. In particular, a Spatio-Temporal Variational Digraph Auto-Encoder model is developed to distinguish the incidents in dynamic traffic flows. To be specific, the model encoder includes two components. The first module, deployed in an edge server, is designed to extract the local contexts from the real-time traffic flow. The dynamic traffic flows will be projected into a spatio-temporal digraph, and in turn addressed by a graph convolutional network for extraction of the deep-seated features. Similarly, the second module is deployed in a central server to capture the spatio-temporal global contexts from historical traffic flows. Finally, the above-concerned contexts are integrated and fed into a model decoder to measure the likelihood of incidents. To testify the proposed paradigm and model, real-world datasets were applied. The experimental results revealed the proposed model outperforms state-of-the-art models in terms of detection accuracy, achieving 26.3% improvement over the best-performing baseline. Furthermore, the proposed paradigm is more efficient in respondence compared with traditional centralized computing, realizing 8x processing speed for the same detection task.

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Data Availibility Statement

The data are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.

Notes

  1. https://pems.dot.ca.gov/

  2. https://www.kaggle.com/alexgude/california-traffic-collision-data-from-switrs/version/1

References

  1. Veres M, Moussa M (2020) Deep learning for intelligent transportation systems: a survey of emerging trends. IEEE Trans Intell Transp Syst 21(8):3152–3168

    Article  Google Scholar 

  2. Ding H, Sze N, Li H, Guo Y (2020) Roles of infrastructure and land use in bicycle crash exposure and frequency: a case study using greater london bike sharing data. Accid Anal Prev 144:105652

    Article  Google Scholar 

  3. Kumar N, Acharya D, Lohani D (2020) An iot-based vehicle accident detection and classification system using sensor fusion. IEEE Internet Things J 8(2):869–880

    Article  Google Scholar 

  4. Iqbal Z, Khan MI (2018) Automatic incident detection in smart city using multiple traffic flow parameters via v2x communication. Int J Distrib Sens Netw 14(11):1550147718815845

    Article  Google Scholar 

  5. Zhao C, Chang X, **e T, Fujita H, Wu J (2022) Unsupervised anomaly detection based method of risk evaluation for road traffic accident. Appl Intell 1–16

  6. Lin Y, Li L, **g H, Ran B, Sun D (2020) Automated traffic incident detection with a smaller dataset based on generative adversarial networks. Accid Anal Prev 144:105628

    Article  Google Scholar 

  7. Lu Y, Wang W, Hu X, Xu P, Zhou S, Cai M (2022) Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks. IEEE Trans Intell Transp Syst

  8. Paule JDG, Sun Y, Moshfeghi Y (2019) On fine-grained geolocalisation of tweets and real-time traffic incident detection. Inf Process Manag 56(3):1119–1132

    Article  Google Scholar 

  9. Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B (2019) Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowl Based Syst 172, 1–14

  10. Lu Y, Ding H, Ji S, Sze N, He Z (2021) Dual attentive graph neural network for metro passenger flow prediction. Neural Comput Appl 33(20):13417–13431

    Article  Google Scholar 

  11. Zhou J, Dai H-N, Wang H, Wang T (2021) Wide-attention and deep-composite model for traffic flow prediction in transportation cyber-physical systems. IEEE Trans Industr Inform 17(5):3431–3440

    Article  Google Scholar 

  12. Lv Z, Zhang S, **u W (2021) Solving the security problem of intelligent transportation system with deep learning. IEEE Trans Intell Transp Syst 22(7):4281–4290

    Article  Google Scholar 

  13. Huang T, Wang S, Sharma A (2020) Highway crash detection and risk estimation using deep learning. Accid Anal Prev 135:105392

    Article  Google Scholar 

  14. Li L, Lin Y, Du B, Yang F, Ran B (2020) Real-time traffic incident detection based on a hybrid deep learning model. Transportmetrica A 1–21

  15. Chen Q, Wang W, Huang K, De S, Coenen F (2021) Multi-modal generative adversarial networks for traffic event detection in smart cities. Expert Syst Appl 177:114939

    Article  Google Scholar 

  16. Chen C, Liu B, Wan S, Qiao P, Pei Q (2021) An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans Intell Transp Syst 22(3):1840–1852

    Article  Google Scholar 

  17. Wang Z, Hu J, Min G, Zhao Z, Wang J (2021) Data-augmentation-based cellular traffic prediction in edge-computing-enabled smart city. IEEE Trans Industr Inform 17(6):4179–4187

    Article  Google Scholar 

  18. Gu Y, Qian ZS, Chen F (2016) From twitter to detector: Real-time traffic incident detection using social media data. Transp Res Part C Emerg Technol 67:321–342

    Article  Google Scholar 

  19. Zhang Z, He Q, Gao J, Ni M (2018) A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C Emerg Technol 86:580–596

    Article  Google Scholar 

  20. Wang L-L, Ngan HY, Yung NH (2018) Automatic incident classification for large-scale traffic data by adaptive boosting svm. Inf Sci 467:59–73

    Article  Google Scholar 

  21. Parsa AB, Movahedi A, Taghipour H, Derrible S, Mohammadian AK (2020) Toward safer highways, application of xgboost and shap for real-time accident detection and feature analysis. Accid Anal Prev 136:105405

  22. Hassija V, Gupta V, Garg S, Chamola V (2020) Traffic jam probability estimation based on blockchain and deep neural networks. IEEE Trans Intell Transp Syst 22(7):3919–3928

    Article  Google Scholar 

  23. Xu X, Shen B, Ding S, Srivastava G, Bilal M, Khosravi MR, Menon VG, Jan MA, Maoli W (2022) Service offloading with deep q-network for digital twinning empowered internet of vehicles in edge computing. IEEE Trans Industr Inform 18(2):1414–1423

  24. Xu X, Shen B, Yin X, Khosravi MR, Wu H, Qi L, Wan S (2021) Edge server quantification and placement for offloading social media services in industrial cognitive iov. IEEE Trans Industr Inform 17(4):2910–2918

    Article  Google Scholar 

  25. Pei X, Yu H, Wang X, Chen Y, Wen M, Wu Y-C (2021) Noma-based pervasive edge computing: secure power allocation for iov. IEEE Trans Industr Inform 17(7):5021–5030

    Article  Google Scholar 

  26. Zhou F, Yang Q, Zhong T, Chen D, Zhang N (2021) Variational graph neural networks for road traffic prediction in intelligent transportation systems. IEEE Trans Industr Inform 17(4):2802-2812

    Article  Google Scholar 

  27. Zhu J, Han X, Deng H, Tao C, Zhao L, Wang P, Lin T, Li H (2022) Kst-gcn: a knowledge-driven spatial-temporal graph convolutional network for traffic forecasting. IEEE Trans. Intell Transp Syst

  28. Jepsen TS, Jensen CS, Nielsen TD (2022) Relational fusion networks: Graph convolutional networks for road networks. IEEE Trans Intell Transp Syst 23(1):418–429

    Article  Google Scholar 

  29. Bui K-HN, Cho J, Yi H (2021) Spatial-temporal graph neural network for traffic forecasting: an overview and open research issues. Appl Intell 1–12

  30. Lai Q, Tian J, Wang W, Hu X (2022) Spatial-temporal attention graph convolution network on edge cloud for traffic flow prediction. IEEE Trans Intell Transp Syst

  31. Wang H, Zhang R, Cheng X, Yang L (2022) Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network. IEEE Trans Intell Transp Syst

  32. Li K, Ye W (2022) Semi-supervised node classification via graph learning convolutional neural network. Appl Intell 1–13

  33. Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2021) Deep learning-based text classification: a comprehensive review. ACM Comput Surv 54(3):1–40

    Article  Google Scholar 

  34. Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, Fieguth P, Cao X, Khosravi A, Acharya UR et al (2021) A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf Fusion 76:243-297

    Article  Google Scholar 

  35. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

    Article  MathSciNet  Google Scholar 

  36. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533-536

    Article  MATH  Google Scholar 

  37. Nousias S, Arvanitis G, Lalos AS, Moustakas K (2021) Fast mesh denoising with data driven normal filtering using deep variational autoencoders. IEEE Trans Industr Inform 17(2):980–990

    Article  Google Scholar 

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Acknowledgements

This research is mainly supported by the Science and Technology Bureau of Guangzhou (Grant No.:SL2022A04J0).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macao SAR, China

    Yuhuan Lu

  2. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao SAR, China

    Yuhuan Lu

  3. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China

    Qinghai Lin

  4. School of Applied Sciences, Macao Polytechnic University, Macao SAR, China

    Haiyang Chi

  5. School of Automotive and Transportation Engineering, GuangDong Polytechnic Normal University, Guangzhou, China

    **-Yong Chen

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  1. Yuhuan Lu
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  2. Qinghai Lin
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  3. Haiyang Chi
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Correspondence to **-Yong Chen.

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Lu, Y., Lin, Q., Chi, H. et al. Automatic incident detection using edge-cloud collaboration based deep learning scheme for intelligent transportation systems. Appl Intell 53, 24864–24875 (2023). https://doi.org/10.1007/s10489-023-04673-7

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