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Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network

  • Jun Zhang (张军)Email author
  • Shenwei Zhao (赵申卫)
  • Yuanqiang Wang (王远强)
  • Xinshan Zhu (朱新山)
Article
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Abstract

The back-propagation neural network (BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm (SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation (PSOBP) and simulated annealing particle swarm optimization back-propagation (SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation (SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation (MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.

Key words

urban traffic short-term traffic flow forecasting social emotion optimization algorithm (SEOA) back-propagation neural network (BPNN) Metropolis rule 

CLC number

TP 183 

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References

  1. [1]
    GUO J H, HUANG W, WILLIAMS B M. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification [J]. Transportation Research Part C, 2014, 43: 50–64.CrossRefGoogle Scholar
  2. [2]
    HOU Y, EDARA P, SUN C. Traffic flow forecasting for urban work zones [J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 1761–1770.CrossRefGoogle Scholar
  3. [3]
    HUANG M L. Intersection traffic flow forecasting based on V-GSVR with a new hybrid evolutionary algorithm [J]. Neurocomputing, 2015, 147: 343–349.CrossRefGoogle Scholar
  4. [4]
    HUWB, YAN L P, LIU K Z, et al. A short-term traffic flow forecasting method based on the hybrid PSO-SVR [J]. Neural Processing Letters, 2015, 43: 155–172.Google Scholar
  5. [5]
    YU B, SONG X L, GUAN F, et al. k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition [J]. Journal of Transportation Engineering, 2016, 142(6) 04016018.CrossRefGoogle Scholar
  6. [6]
    ZHENG W Z, LEE D H, SHI Q X. Short-term freeway traffic flow prediction: Bayesian combined neural network approach [J]. Journal of Transportation Engineering, 2006, 132(2): 114–121.CrossRefGoogle Scholar
  7. [7]
    SMITH B L, DEMETSKY M J. Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches [C]//Proceedings of IEEE International Conference on Systems, Man and Cybernetics. IEEE: [s.n.], 1994: 1706–1709.Google Scholar
  8. [8]
    CHAN Y K, DILLON T S. Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models [C]//Proceedings of IEEE International Joint Conference on Neural Networks. Beijing, China: IEEE, 2014: 35–41.Google Scholar
  9. [9]
    TONG G, FAN C L, CUI F Y, et al. Fuzzy neural network model applied in the traffic flow prediction [C]//Proceedings of the 2006 IEEE International Conference on Information Acquisition. Weihai, China: IEEE, 2006: 1229–1233.Google Scholar
  10. [10]
    PARK B, MESSER C J, URBANIK II T. Short-term freeway traffic volume forecasting using radial basis function neural network [J]. Transportation Research Record:Journal of the Transportation Research Board, 1998, 1651: 39–47.CrossRefGoogle Scholar
  11. [11]
    DIA H. An object-oriented neural network approach to short-term traffic forecasting [J]. European Journal of Operational Research, 2001, 131(2): 253–261.CrossRefzbMATHGoogle Scholar
  12. [12]
    ZHANG N, ZHANG Y L, LU H T. Seasonal autoregressive integrated moving average and support vector machine models: Prediction of short term traffic flow on freeways [J]. Transportation Research Record: Journal of the Transportation Research Board, 2011, 2215: 85–92.CrossRefGoogle Scholar
  13. [13]
    HONG W C. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm [J]. Neurocomputing, 2011, 74(12/13): 2096–2107.CrossRefGoogle Scholar
  14. [14]
    LI M W, HONG W C, KANG H G. Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm [J]. Neurocomputing, 2013, 99: 230–240.CrossRefGoogle Scholar
  15. [15]
    CHENG W, FENG P. Network traffic prediction algorithm research based on PSO-BP neural network [C]//Proceedings of International Conference on Intelligent Systems Research and Mechatronics Engineering. [s.l.]: Atlantis Press, 2015: 1239–1243.Google Scholar
  16. [16]
    KE L J, ZHANG Q F, BATTITI R. MOEA/D-ACO: A multi-objective evolutionary algorithm using decomposition and ant colony [J]. IEEE Transactions on Cybernetics, 2013, 43(6): 1845–1859.CrossRefGoogle Scholar
  17. [17]
    CUI Z H, CAI X J. Using social cognitive optimization algorithm to solve nonlinear equations [C]//Proceedings of the 9th IEEE International Conference on Cognitive Informatics. Beijing, China: IEEE, 2010: 199–203.Google Scholar
  18. [18]
    XU Y C, CUI Z H, ZENG J C. Social emotional optimization algorithm for nonlinear constrained optimization problems [C]//Proceedings of the 1st International Conference on Swarm, Evolutionary, and Memetic Computing. Berlin, Germany: Springer-Verlag, 2011: 583–590.Google Scholar
  19. [19]
    ZHANG Y Q, ZHANG P L. Machine training and parameter settings with social emotional optimization algorithm for support vector machine [J]. Pattern Recognition Letters, 2015, 54: 36–42.CrossRefGoogle Scholar
  20. [20]
    METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et al. Equations of states calculations for fast computing machines [J]. Journal of Chemical Physics, 1953, 21(6): 1087–1091.CrossRefGoogle Scholar
  21. [21]
    FANG J Y. Hybrid group search optimization algorithm and its application research [D]. Taiyuan, China: Department of Computer Science and Technology, Taiyuan University of Science and Technology, 2010 (in Chinese).Google Scholar
  22. [22]
    WONG W H, LIANG F M. Dynamic weighting in Monte Carlo and optimization [J]. Proceedings of the National Academy of Sciences of the United States of America, 1997, 94(26): 14220–14224.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jun Zhang (张军)
    • 1
    Email author
  • Shenwei Zhao (赵申卫)
    • 1
  • Yuanqiang Wang (王远强)
    • 1
  • Xinshan Zhu (朱新山)
    • 1
  1. 1.School of Electrical Engineering and AutomatonTianjin UniversityTianjinChina

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