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Neural Computing and Applications

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01-10-2016 | Original Article

Research on prediction of traffic flow based on dynamic fuzzy neural networks

Author: Haitao Li

Published in: Neural Computing and Applications | Issue 7/2016

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Abstract

Combining the advantages of the neural network and fuzzy system, this paper makes a further research on the dynamic fuzzy neural networks (D-FNN) traffic flow prediction. Instead of being in consistence with growth of the input number, the fuzzy rule number of the D-FNN increases exponentially in the whole training network structure. In particular, this method can establish a required network structure automatically. This method is applied to the traffic flow time series to analyze and compare the predicting performance of the predicting model based on the neural network method and the adaptive neural fuzzy inference system by combining with the chaos theory. The simulation result shows that this method is quite effective and can improve the predicting accuracy.

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Smith B (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transp Res Part C 10(4):303–321CrossRef

Rice J (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5(3):200–207CrossRef

Giovanni P (2006) A processing architecture for associative short-term memory in electronic noses. Meas Sci Technol 17(11):3066–3072CrossRef

Vladimir N (2014) Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environ Impact Assess Rev 49:24–30CrossRef

Marfia G, Roccetti M (2010) Vehicular congestion detection and short-term forecasting: a new model with results. IEEE Trans Veh Technol 60(7):2936–2948CrossRef

Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C 13(3):211–234CrossRef

Li MT, Yuan J (2013) WT-AOSVR models for short-time traffic flow prediction. Comput Appl Softw 1(30):277–280

Hamed M, Al-Masaeid H (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng (ASCE) 121(3):249–254CrossRef

Okutani I, Stephanedes YJ (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transp Res B 18(1):1–11CrossRef

10.

Hong WC, Pai PF (2006) Predicting engine reliability by support vector machines. Int J Adv Manuf Technol 28(1–2):154–161CrossRef

11.

Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21:583–593CrossRef

12.

Zhang WB, Hu HZ, Liu WJ (2007) traffic flow forecast based on type-2 fuzzy logic approach. J Xi’an Jiaotong Univ 41(10):1160–1163MATH

13.

Loukas D, Theodore T, Antony S (2008) Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transp Res Part C 16(5):554–573CrossRef

14.

Abdulhai B, Porwal H, Recker W (2002) Short term traffic-flow forecasting using neuro-genetic algorithms. J Intell Transp Syst Technol Plann Oper 7(1):3–41CrossRefMATH

15.

Park B (2002) Hybrid neuro-fuzzy application in short term freeway traffic volume predicting. Transp Res Rec 1802:190–196CrossRef

16.

Zhang L, Liu QC, Yang WC (2013) An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia—Soc Behav Sci 96:653–662CrossRef

17.

Zhang C, Sun S, Yu G (2004) Short-term traffic flow forecasting using expanded Bayesian network for incomplete data. In: Proceedings international symposium on neural networks, Dalian, China. Lecture Notes in Computer Science (LNCS 3174), Springer, Berlin

18.

Enrique C, Jose MM, Santos SC (2008) Predicting traffic flow using Bayesian networks. Transp Res Part B 42(5):482–509CrossRef

19.

Hodge V, Jackson T, Austin J (2012) A binary neural network framework for attribute selection and prediction. In: Proceedings 4th international conference on neural computation theory and applications (NCTA 2012), SciTePress, Barcelona, pp 510–515

20.

Victoria JH, Rajesh K, Jim A et al (2014) Short -term prediction of traffic flow using a binary neural network. Neural Comput Appl 25:1639–1655CrossRef

21.

Hou Y, Mai YM (2013) Chaotic prediction for traffic flow of improved BP neural network. Indones J Electr Eng 11(3):1682–1690

22.

Lu JJ, Wang ZQ (2006) Internet traffic data flow forecast by RBF neural network based on phase space reconstruction. Trans Nanjing Univ Aeronaut Astronaut 23(4):316–322MATH

23.

Vlahogianni EI, Karlaftis MG, Golias JC (2007) Spatio-temporal urban traffic volume forecasting using genetically optimized modular networks. Comput-Aided Civil Infrastruct Eng 22(5):317–325CrossRef

24.

Zhong M, Sharma S, Lingras P (2005) Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models. J Comput Civil Eng 19(1):94–103CrossRef

25.

Vlahogianni EI (2009) Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J Intell Transp Syst 13(2):73–84CrossRef

26.

Roger J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRef

27.

Zhu WF, Zhao SJ (2007) Optimal design of structure for neural networks based on rough sets. Comput Eng Des 28(17):4210–4212

28.

Fu H, Xu LH, Hu G et al (2010) Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system. Control Theory Appl 12(27):1637–1640

29.

Er MJ, Wu SQ, Gao YA (2001) fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594CrossRef

30.

Er MJ, Wu SQ (2002) A fast learning algorithm for parsimonious fuzzy neural system [J]. Fuzzy Sets Syst 126(3):337–351MathSciNetCrossRefMATH

31.

Tan W, Wang YN (2008) Fuzzy neural network control theory and method of chaotic system. Science Press, Beijing, pp 108–150

32.

Yu JH, Peng YH (2011) Predictability study of ENSO with mutual information and Cao methods. Meteorol Sci Technol 39(1):9–12MathSciNet

33.

Zhang SQ, Jia J (2010) Study on the parameters determination for reconstructing phase-space in chaos time series. Acta Phys Sin 59(3):1576–1582MATH

34.

Lv JH, Lu JN (2001) The chaotic time series analysis and its application. Wuhan University Press, Wuhan, pp 15–93

Title: Research on prediction of traffic flow based on dynamic fuzzy neural networks
Author: Haitao Li
Publication date: 01-10-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2016
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1991-z

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