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Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

GCN based on time and space is an essential part of smart city construction because it can capture the spatiotemporal dynamics and effectively analyze the traffic data to get the best prediction results. In the specific operation of the model, the adjustment and optimal selection of super parameters can make the model provide the best results, thus saving time, cost and computing power. When it comes to the prediction scenarios with low computational power and urgent demand, the existing super parameter search methods and optimization models lack efficiency and accuracy. Therefore, this paper proposes a super parameter search and optimization method based on cross validation, which can efficiently and accurately optimize the parameters, and select the best parameters by using the similarity between the learning and training errors corresponding to each super parameter To improve the prediction ability of the model. Through the verification of the actual data set, the model runs well, and can provide the best prediction results for the traffic flow and other scenarios dominated by spatiotemporal state.

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References

  1. Zhang, K., Chen, L., An, Y., et al.: A QoE test system for vehicular voice cloud services. Mob. Netw. Appl. (2019). https://doi.org/10.1007/s11036-019-01415-3

    Article  Google Scholar 

  2. Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196–207 (2019)

    Google Scholar 

  3. Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 1–21 (2019)

    Google Scholar 

  4. Chen, L., Jiang, D., Bao, R., Xiong, J., Liu, F., Bei, L.: MIMO Scheduling effectiveness analysis for bursty data service from view of QoE. Chin. J. Electron. 26(5), 1079–1085 (2017)

    Article  Google Scholar 

  5. Jiang, D., Wang, Y., Lv, Z., et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inform. 16(2), 1310–1320 (2020)

    Google Scholar 

  6. Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1–12 (2018)

    MathSciNet  Google Scholar 

  7. Chen, L., et al.: A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1), 15408–15419 (2018)

    Article  Google Scholar 

  8. Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system under QoS constraint: an effective capacity perspective. Mob. Netw. Appl. (2020). https://doi.org/10.1007/s11036-019-01414-4

    Article  Google Scholar 

  9. Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. 1–12 (2019)

    Google Scholar 

  10. Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)

    Article  Google Scholar 

  11. Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet Things J. 3(6), 1437–1447 (2016)

    Article  Google Scholar 

  12. Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220, 160–169 (2017)

    Article  Google Scholar 

  13. Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent optimization-based reliable energy-efficient networking in cloud services for IIoT networks. IEEE J. Select. Areas Commun. 1–6 (2019)

    Google Scholar 

  14. Jiang, D., Wang, W., Shi, L., et al.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 5(3), 1–12 (2018)

    Google Scholar 

  15. Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 13(5), 1–23 (2018)

    Google Scholar 

  16. Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. 1–10 (2019)

    Google Scholar 

  17. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. 1–10 (2019)

    Google Scholar 

  18. Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mob. Netw. Appl. 1–11 (2019)

    Google Scholar 

  19. Huo, L., Jiang, D., Zhu, X., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int. J. Commun. Syst. 1–12, (2019)

    Google Scholar 

  20. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  21. Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)

    Article  Google Scholar 

  22. Moravˇcik, M., et al.: DeepStack: Expert-level artifificial intelligence in heads-up no-limit poker. Science 356(6337), 508–513 (2017)

    Article  MathSciNet  Google Scholar 

  23. Park, D., Rilett, L.R.: Forecasting freeway link travel times with a multilayer feedforward neural network. Comput.-Aided Civil Infrastruct. Eng. 14(5), 357–367 (1999)

    Google Scholar 

  24. Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffific flow prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)

    Article  Google Scholar 

  25. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffific flow prediction. In: 31st Youth Academic Annual Conference China Association Automation (YAC), Wuhan, China, pp. 324–328 (2016)

    Google Scholar 

  26. Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: Freeway travel time prediction with state-space neural networks: modeling statespace dynamics with recurrent neural networks. Transp. Res. Rec. 1811(1), 30–39 (2002)

    Article  Google Scholar 

  27. Zhao, L., Song, Y., Zhang, C., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2018)

    Google Scholar 

  28. Ding, L., Huang, Z., Chen, G.: An FPGA implementation of GCN with sparse adjacency matrix. In: 2019 IEEE 13th International Conference on ASIC (ASICON) (2019)

    Google Scholar 

  29. Zheng, J., Li, D.: GCN-TC: combining trace graph with statistical features for network traffic classification. In: 2019 IEEE International Conference on Communications (ICC) (2019)

    Google Scholar 

  30. Li, Z., Xiong, G., Chen, Y.: A hybrid deep learning approach with GCN and LSTM for traffic flow prediction. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (2019)

    Google Scholar 

  31. Reddi, S.J., Kale, S., Kumar, S.: On the Convergence of Adam and Beyond (2019)

    Google Scholar 

  32. Keskar, N.S., Socher, R.: Improving Generalization Performance by Switching from Adam to SGD (2017)

    Google Scholar 

  33. Hoffer, E., Hubara, I., Soudrym D.: Train longer, generalize better: closing the generalization gap in large batch training of neural networks. In: Advances in Neural Information Processing Systems, pp. 1731–1741 (2017)

    Google Scholar 

  34. Goyal, P., Dollar, P., Girshick, R.B., et al.: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. arXiv: Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  35. Keskar, N.S., Socher, R.: Improving generalization performance by switching from adam to sgd. arXiv preprint arXiv:1712.07628 (2017)

  36. Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond (2018)

    Google Scholar 

  37. Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472, IEEE (2017)

    Google Scholar 

  38. Smith, S.L., Kindermans, P.J., Ying, C., et al.: Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)

  39. Cardona-Escobar, A.F., Giraldo-Forero, A.F., Castro-Ospina, A.E., Jaramillo-Garzón, F.A.: Efficient hyperparameter optimization in convolutional neural networks by learning curves prediction. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 143–151. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75193-1_18

    Chapter  Google Scholar 

  40. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral fifiltering. Proc. Adv. Neural Inf. Process. Syst., 3844–3852 (2016)

    Google Scholar 

  41. Kipf, T.N., Welling, M.: Semi-supervised classifification with graph convolutional networks (2016). https://arxiv.org/abs/1609.02907

  42. Bruna, J., Zaremba, W., Szlam, A., Lecun, Y.: Spectral networks and locally connected networks on graphs. https://arxiv.org/abs/1312.6203 (2013)

  43. Ma, Y., et al: High performance graph convolutional networks with applications in testability Analysis. In: ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, pp. 18:1–18:6 (2019)

    Google Scholar 

  44. Forecasting road traffic speeds by considering area-wide spatio temporal dependencies based on a graph convolutional neural network (GCN). In: 2019 Chinese Control Conference (CCC) (2019)

    Google Scholar 

  45. Wu, C., Chai, L., Yang, J., Sheng, Y.: Facial expression recognition using convolutional neural network on graphs. In: The 38th China Control Conference, pp. 90–94 (2019)

    Google Scholar 

  46. Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. https://arxiv.org/abs/1409.1259 (2014)

  47. Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. https://arxiv.org/abs/1412.3555 (2014)

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Acknowledgements

This work is partly supported by Jiangsu technology project of Housing and Urban-Rural Development (No. 2018ZD265, No. 2019ZD039, No. 2019ZD040, No. 2019ZD041).

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Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221018, China

    Jing Huang, Lei Chen, Yuan An, Kailiang Zhang & Ping Cui

Authors
  1. Jing Huang
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  2. Lei Chen
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  3. Yuan An
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  4. Kailiang Zhang
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  5. Ping Cui
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Corresponding author

Correspondence to Lei Chen .

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Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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Huang, J., Chen, L., An, Y., Zhang, K., Cui, P. (2021). Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_53

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  • DOI: https://doi.org/10.1007/978-3-030-72792-5_53

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