A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

[1]  Gang Wang,et al.  An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease , 2016, Neurocomputing.

[2]  B. LeBaron,et al.  Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence , 1991 .

[3]  Xueli An,et al.  A chaos embedded GSA-SVM hybrid system for classification , 2014, Neural Computing and Applications.

[4]  Rajesh Krishnan,et al.  A computationally efficient two-stage method for short-term traffic prediction on urban roads , 2013 .

[5]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[6]  Dimitrios S. Dendrinos,et al.  Traffic-flow dynamics: A search for chaos , 1994 .

[7]  Longbing Cao,et al.  T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Kara M. Kockelman,et al.  Chaos Theory and Transportation Systems: Instructive Example , 2004 .

[9]  Anatoly Zhigljavsky,et al.  Predicting daily exchange rate with singular spectrum analysis , 2010 .

[10]  Naif Alajlan,et al.  Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[11]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[12]  Yuehua Huang,et al.  Short-term wind power prediction based on LSSVM–GSA model , 2015 .

[13]  Stephen Graham Ritchie,et al.  TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES , 1993 .

[14]  Hossein Hassani,et al.  MULTIVARIATE SINGULAR SPECTRUM ANALYSIS: A GENERAL VIEW AND NEW VECTOR FORECASTING APPROACH , 2013 .

[15]  Zhaohong Deng,et al.  Multitask TSK Fuzzy System Modeling by Mining Intertask Common Hidden Structure , 2015, IEEE Transactions on Cybernetics.

[16]  Su Yang,et al.  Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection , 2015, PloS one.

[17]  H. S. Kim,et al.  Nonlinear dynamics , delay times , and embedding windows , 1999 .

[18]  Hong Yan,et al.  Fast prediction of protein-protein interaction sites based on Extreme Learning Machines , 2014, Neurocomputing.

[19]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[20]  Min Han,et al.  Online sequential extreme learning machine with kernels for nonstationary time series prediction , 2014, Neurocomputing.

[21]  Zhaohong Deng,et al.  Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods , 2014, IEEE Transactions on Cybernetics.

[22]  Henry X. Liu,et al.  Short Term Traffic Forecasting Using the Local Linear Regression Model , 2002 .

[23]  JeongYoung-Seon,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009 .

[24]  Ali Selamat,et al.  A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction , 2014, Inf. Fusion.

[25]  Seyed Hossein Iranmanesh,et al.  A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting , 2015, Inf. Sci..

[26]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[27]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[28]  Shahaboddin Shamshirband,et al.  Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran , 2015 .

[29]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[30]  Huan Wang,et al.  A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR , 2015, Neural Processing Letters.

[31]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[32]  Henry X. Liu,et al.  Use of Local Linear Regression Model for Short-Term Traffic Forecasting , 2003 .

[33]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

[34]  Danilo Comminiello,et al.  Online Sequential Extreme Learning Machine With Kernels , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Gottfried Mayer-Kress,et al.  Dimensions and Entropies in Chaotic Systems , 1986 .

[36]  Haitham Al-Deek,et al.  Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models , 2009, J. Intell. Transp. Syst..

[37]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[38]  Yizhang Jiang,et al.  Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification , 2015, Appl. Soft Comput..

[39]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[40]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[41]  M. Rosenstein,et al.  Reconstruction expansion as a geometry-based framework for choosing proper delay times , 1994 .

[42]  Amaury Lendasse,et al.  Bankruptcy prediction using Extreme Learning Machine and financial expertise , 2014, Neurocomputing.

[43]  Yuhong Yang,et al.  Cross-validation for selecting a model selection procedure , 2015 .

[44]  Weiping Zhang,et al.  Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm , 2013, Knowl. Based Syst..

[45]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[46]  Lawrence W. Lan,et al.  TESTING AND PREDICTION OF TRAFFIC FLOW DYNAMICS WITH CHAOS , 2003 .

[47]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[48]  N. Bigdeli,et al.  Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA) , 2011 .

[49]  Joachim Holzfuss,et al.  Approach to error-estimation in the application of dimension algorithms , 1986 .

[50]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting , 2005 .

[51]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .

[52]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[53]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[54]  Qunxiong Zhu,et al.  A data-attribute-space-oriented double parallel (DASODP) structure for enhancing extreme learning machine: Applications to regression datasets , 2015, Eng. Appl. Artif. Intell..

[55]  邵春福,et al.  A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques , 2015 .

[56]  Hans van Lint,et al.  Short-Term Traffic and Travel Time Prediction Models , 2012 .

[57]  Lahouari Ghouti,et al.  Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines , 2013, ANT/SEIT.

[58]  Qiang Zhang,et al.  Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting , 2011 .

[59]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[60]  S. Sanei,et al.  An adaptive singular spectrum analysis approach to murmur detection from heart sounds. , 2011, Medical engineering & physics.

[61]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[62]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[63]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[64]  Skander Soltani,et al.  On the use of the wavelet decomposition for time series prediction , 2002, ESANN.

[65]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[66]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[67]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[68]  Mauro Garavello,et al.  Traffic Flow on Networks , 2006 .

[69]  Jorge A. Laval,et al.  Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model , 2008 .

[70]  F. Takens.  Detecting strange attractors in turbulence , 1981 .