Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Application of an Optimized Support Vector Regression Algorithm in Short-Term Traffic Flow Prediction

  • Ruibo, Ai (College of Sciences, Qiqihar University) ;
  • Cheng, Li (College of Computer and Control Engineering, Qiqihar University) ;
  • Na, Li (Public Foreign Language Teaching and Research Department, Qiqihar University)
  • Received : 2021.12.08
  • Accepted : 2022.07.08
  • Published : 2022.12.31
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Abstract

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

Keywords

Acknowledgement

This research was funded by the Education Department of Heilongjiang Province of China (No. 135309463 and 145109129).

References

  1. L. Chen, L. Zheng, J. Yang, D. Xia, and W. Liu, "Short-term traffic flow prediction: from the perspective of traffic flow decomposition," Neurocomputing, vol. 413, pp. 444-456, 2020. https://doi.org/10.1016/j.neucom.2020.07.009
  2. C. Li and P. Xu, "Application on traffic flow prediction of machine learning in intelligent transportation," Neural Computing and Applications, vol. 33, no. 2, pp. 613-624,
  3. M. Chen, R. Chen, F. Cai, W. Li, N. Guo, and G. Li, "Short-term traffic flow prediction with recurrent mixture density network," Mathematical Problems in Engineering, vol. 2021, article no. 6393951, 2021. https://doi.org/10.1155/2021/6393951
  4. Q. Cao and S. Fujita, "Cost-effective replication schemes for query load balancing in DHT-based peer-topeer file searches," Journal of Information Processing Systems, vol. 10, no. 4, pp. 628-645, 2014. https://doi.org/10.3745/JIPS.03.0020
  5. J. Abdi, B. Moshiri, B. Abdulhai, and A. K. Sedigh, "Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning," Neural Computing and Applications, vol. 23, no. 1, pp. 141-159, 2013. https://doi.org/10.1007/s00521-012-0977-3
  6. B. M. Williams and L. A. Hoel, "Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results," Journal of Transportation Engineering, vol. 129, no. 6, pp. 664-672, 2003. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
  7. J. Liu and W. Guan, "A summary of traffic flow forecasting methods," Journal of Highway and Transportation Research and Development, vol. 21, no. 3, pp. 82-85, 2004. https://doi.org/10.3969/j.issn.1002-0268.2004.03.022
  8. B. Ghosh, B. Basu, and M. O'Mahony, "Multivariate short-term traffic flow forecasting using time-series analysis," IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 2, pp. 246-254, 2009. https://doi.org/10.1109/TITS.2009.2021448
  9. L. Cai, Z. Zhang, J. Yang, Y. Yu, T. Zhou, and J. Qin, "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, vol. 536, article no. 122601, 2019. https://doi.org/10.1016/j.physa.2019.122601
  10. Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, "Traffic flow prediction with big data: a deep learning approach," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865-873, 2015. https://doi.org/10.1109/TITS.2014.2345663
  11. M. J. Ebadi, A. Hosseini, and M. M. Hosseini, "A projection type steepest descent neural network for solving a class of nonsmooth optimization problems," Neurocomputing, vol. 235, pp. 164-181, 2017. https://doi.org/10.1016/j.neucom.2017.01.010
  12. Z. Yan, H. Yang, F. Li, and Y. Lin, "A deep learning approach for short-term airport traffic flow prediction," Aerospace, vol. 9, no. 1, article no. 11, 2021. https://doi.org/10.3390/aerospace9010011
  13. F. Zhang, T. Y. Wu, Y. Wang, R. Xiong, G. Ding, P. Mei, and L. Liu, "Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction," IEEE Access, vol. 8, pp. 104555-104564, 2020. https://doi.org/10.1109/access.2020.2999608
  14. Q. Chen, Y. Song, and J. Zhao, "Short-term traffic flow prediction based on improved wavelet neural network," Neural Computing and Applications, vol. 33, no. 14, pp. 8181-8190, 2021. https://doi.org/10.1007/s00521-020-04932-5
  15. Y. Shao, Y. Zhao, F. Yu, H. Zhu, and J. Fang, "The traffic flow prediction method using the incremental learning-based CNN-LTSM model: the solution of mobile application," Mobile Information Systems, vol. 2021, article no. 5579451, 2021. https://doi.org/10.1155/2021/5579451