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Short-Term Traffic Condition Prediction Based on Multi-source Data Fusion

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

Accurate prediction of the short-term traffic condition can help to relieve the pressure of traffic and optimize the intelligent transportation system. Traditional traffic condition prediction is mainly based on historical time-series data only, and some sudden factors such as weather conditions are usually ignored. As a result, the accuracy of prediction is compromised. To address this issue, we propose a recurrent neural network to integrate the information of weather situations and road conditions to predict traffic conditions. Experimental results show that compared to the baseline methods using time-series data only, our proposed method can improve the prediction accuracy up to 5.6%.

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References

  1. Wang, Y., Goldmines, N., Leclercq, L.: Recent advances in ITS, traffic flow theory, and network operations. Transp. Res. Part C Emerg. Technol. 68, 507–508 (2016)

    Article  Google Scholar 

  2. Nanni, M., Kuijpers, B., Krner, C., et al.: Spatiotemporal data mining. Mob. Data Min. Priv. 27(3), 187–190 (2008)

    Google Scholar 

  3. Li, W., Chen, S., Wang, X., et al.: A hybrid approach for short-term traffic flow forecasting based on similarity identification. Mod. Phys. Lett. B 35(13), 2150212 (2021)

    Article  MathSciNet  Google Scholar 

  4. Qu, Z., Li, H., Li, Z., et al.: Short-term traffic flow forecasting method with M-B-LSTM hybrid network. IEEE Trans. Intell. Transp. Syst. 99, 1–11 (2020)

    Google Scholar 

  5. Feng, X., Ling, X., Zheng, H., et al.: Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 99, 1–13 (2018)

    Google Scholar 

  6. Wu, P., Huang, Z., Pian, Y., et al.: A combined deep learning method with attention-based LSTM model for short-term traffic speed forecasting. J. Adv. Transp. 2020(4), 1–15 (2020)

    Google Scholar 

  7. Sánchez, J.M., et al.: Predicting using box-Jenkins, nonparametric, and bootstrap techniques. Technometrics 37(3), 303–310 (1995)

    MathSciNet  MATH  Google Scholar 

  8. Guo, J., Huang, W., Williams, B.M.: Adaptive kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp. Res. Part C Emerg. Technol 43, 50–64 (2014)

    Article  Google Scholar 

  9. Zhang, Q., Benveniste, A.: Wavelet networks. IEEE Transp. Neural Netw. 3(6), 889–898 (1992)

    Article  Google Scholar 

  10. Guan, HS., Ma, W.G., Meng, Y.Y.: Traffic flow prediction based on hierarchical genetic optimized algorithm. In: 3rd International Conference on Tractor & Farm Transporter, vol. 37, p. 121 (2008)

    Google Scholar 

  11. Davis, G.A., Nihan, N.L.: Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng. 117(02), 178–188 (1991)

    Article  Google Scholar 

  12. Cui, F.: Traffic flow prediction based on BP neural network. In: Intelligent Systems and Applications (ISA), pp. 1–4 (2010)

    Google Scholar 

  13. Liu, R.R., Hong, F., et al.: Short-term traffic flow prediction based on deep circulation neural network. J. Phys. Conf. Ser. 1176, 032020 (2019)

    Article  Google Scholar 

  14. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  15. Qiao, S., Sun, R., Fan, G., Liu, J.: Short-term traffic flow forecast based on parallel long short-term memory neural network. In: 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 253–257. IEEE (2017)

    Google Scholar 

  16. Wu, Y., Tan, H., Qin, L., et al.: A hybrid deep learning based traffic flow prediction method and its understanding. Transp. Res. Part C Emerg. Technol. 90(1), 166–180 (2018)

    Article  Google Scholar 

  17. Hu, X., Wei, X., Gao, Y., et al.: An attention-mechanism-based traffic flow prediction scheme for smart city. In: 15th IEEE International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1822–1827. IEEE (2019)

    Google Scholar 

  18. Dey, R., Salemt, F.M.: Gate-variants of gated recurrent unit (GRU) neural networks. In: IEEE International Midwest Symposium on Circuits & Systems, pp. 1597–1600 (2017)

    Google Scholar 

  19. Jiang, H., Ye, C., Deng, X., et al.: Deep learning for short-term traffic conditions prediction. In: International Conference on Service Science (ICSS), pp. 70–75. IEEE (2020)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Key Research and Development Program of Hainan Province under grant No. ZDYF2020008ˈthe Natural Science Foundation of Hainan Province under the grant No. 2019RC088, 2019CXTD400, and grants from State Key Laboratory of Marine Resource Utilization in South China Sea and Key Laboratory of Big Data and Smart Services of Hainan Province.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Xiaoru Deng, Hui Zhou & Chunyang Ye

  2. School of Computer Management, Hefei University of Technology, Hefei, 230009, China

    Xiaoran Yang

Authors
  1. Xiaoru Deng
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  2. Hui Zhou
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  3. Xiaoran Yang
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  4. Chunyang Ye
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Corresponding author

Correspondence to Hui Zhou .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Deng, X., Zhou, H., Yang, X., Ye, C. (2021). Short-Term Traffic Condition Prediction Based on Multi-source Data Fusion. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_29

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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