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Short-term traffic flow prediction based on improved wavelet neural network

  • S. I: Intelligent Computing Methodologies in Machine learning for IoT Applications
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Abstract

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%.

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References

  1. Wang Y, Lu Z, Qu Y et al (2016) Improving prediction performance of GPS satellite clock bias based on wavelet neural network[J]. GPS Solut 21(2):1–12

    Google Scholar 

  2. Duan H, Xiao X, Lei G et al (2017) Hamiltonian cycles decomposition of two cycles’s composite[J]. J Discrete Math Sci Cryptogr 20(5):1241–1250

    Article  MathSciNet  Google Scholar 

  3. Yang H-F, Dillon TS, Chen YP (2017) Optimized structure of the traffic flow forecasting model with a deep learning approach[J]. IEEE Trans Neural Netw Learn Syst 28(10):2371–2381

    Article  Google Scholar 

  4. He W, Zhong C, Sotelo MA et al (2017) Short-term vessel traffic flow forecasting by using an improved Kalman model[J]. Clust Comput 10:1–10

    Google Scholar 

  5. Zhou T, Han G, Xu X et al (2018) A learning-based multimodel integrated framework for dynamic traffic flow forecasting[J]. Neural Process Lett 5:1–24

    Google Scholar 

  6. Zou Weidong, Xia Yuanqing (2019) Back propagation bidirectional extreme learning machine for traffic flow time series prediction. Neural Comput Appl 31(11):7401–7414

    Article  Google Scholar 

  7. Liu Mei, Agarwal Jitendra, Blockley David (2016) Vulnerability of road networks[J]. Civil Eng Environ Syst 33(2):1–29

    Article  Google Scholar 

  8. Zhang XB, Wang JZ, Zhang KQ (2017) Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm[J]. Electr Power Syst Res 146(2):270–285

    Article  Google Scholar 

  9. Dong Shi, Li Ruixuan (2019) Traffic identification method based on multiple probabilistic neural network model. Neural Comput Appl 31(2):473–487

    Article  Google Scholar 

  10. Lun-hui XU, Xiang-xue W (2016) Application of SVD-based optimized robust UKF algorithm to estimation of short-term traffic flow state[J]. J Southwest Jiaotong Univ 44(12):44–52

    Google Scholar 

  11. Chen D, Hu M, Zhang H et al (2016) Short-term traffic flow prediction of airspace sectors based on bayesian estimation theory[J]. Xinan Jiaotong Daxue Xuebao/J Southwest Jiaotong Univ 51(4):807–814

    MathSciNet  MATH  Google Scholar 

  12. Shang Q, Yang Z, Zhang W et al (2016) Short-term traffic flow prediction based on singular spectrum analysis and CKF-LSSVM[J]. J Jilin Univ 46(6):1792–1798

    Google Scholar 

  13. Y Xing, X-J Ban, R Liu (2018) A short-term traffic flow prediction method based on kernel extreme learning machine[C]. In: 2018 IEEE international conference on big data and smart computing (BigComp). IEEE

  14. Huang Min-Liang (2015) Intersection traffic flow forecasting based on ν-GSVR with a new hybrid evolutionary algorithm[J]. Neurocomputing 147(1):343–349

    Article  Google Scholar 

  15. Hu W, Wang H, Qiu Z, Nie C, Yan L (2018) A quantum particle swarm optimization driven urban traffic light scheduling model. Neural Comput Appl 29(3):901–911

    Article  Google Scholar 

  16. Xueneng SU, Tianqi LIU, Hongqian CAO et al (2017) A multiple distributed BP neural networks approach for short-term load forecasting based on hadoop framework[J]. Proc CSEE 37(17):4966–4973

    Google Scholar 

  17. Qiao Y, Zhao B, Fang C et al (2017) Study of ramp control method for urban expressways using improvised ALINEA algorithm[J]. J Southwest Jiaotong Univ 52(5):1001–1007

    Google Scholar 

  18. Abdi J, Moshiri B (2015) Application of temporal difference learning rules in short-term traffic flow prediction[J]. Expert Syst 32(1):49–64

    Article  Google Scholar 

  19. Yang H, Dillon TS, Chang E et al (2019) Optimized configuration of exponential smoothing and extreme learning machine for traffic flow forecasting[J]. IEEE Trans Ind Inf 15(1):23–34

    Article  Google Scholar 

  20. Tang L, Zhao Y, Cabrera J et al (2018) Forecasting short-term passenger flow: an empirical study on Shenzhen metro[J]. IEEE Trans Intell Transp Syst 22(99):1–10

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Innovation and Entrepreneurship Project for Overseas High-level Talents of Shenzhen (KQJSCX20180329191021388), a Research Grant in Shenzhen Polytechnic (No. 6020320004K), National Science Foundation Project: Research on Collaborative Innovation System of the Pearl River-Xijiang River Economic Belt under Supply-side Reform (No. 17XGL006), Wuzhou University Foundation Project: Research on transformation and upgrading of manufacturing industry for the Pearl River-Xijiang River Economic Belt (No. 2017A002).

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Authors and Affiliations

  1. School of Computer Engineering, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China

    Qiuxia Chen

  2. School of Business, Wuzhou University, Wuzhou, 543002, Guangxi, China

    Ying Song

  3. School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China

    Jianfeng Zhao

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  1. Qiuxia Chen
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  2. Ying Song
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  3. Jianfeng Zhao
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Correspondence to Ying Song.

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Chen, Q., Song, Y. & Zhao, J. Short-term traffic flow prediction based on improved wavelet neural network. Neural Comput & Applic 33, 8181–8190 (2021). https://doi.org/10.1007/s00521-020-04932-5

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  • DOI: https://doi.org/10.1007/s00521-020-04932-5

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