Application of hybrid genetic algorithm and simulated annealing in a SVR traffic flow forecasting model

Due to complex nonlinear data pattern in time series regression, forecasting techniques had been categorized in different ways, and the literature is also full of differing opinions, thus, it is difficult to make a general conclusion. In the recent years, the support vector regression (SVR) model has been widely used to solve nonlinear time series regression problems. This investigation presents a short-term traffic forecasting model by employing SVR with genetic algorithm and simulated annealing algorithm (GA-SA) to determine the suitable parameter combination in the SVR model. Consequently, a numerical example of traffic flow values from northern Taiwan is used to demonstrate the forecasting performance of the proposed SVRGA-SA model is superior to the seasonal autoregressive integrated moving average (SARIMA) time series model.

[1]  Lijuan Cao,et al.  Dynamic support vector machines for non-stationary time series forecasting , 2002, Intell. Data Anal..

[2]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[3]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[4]  S. G. Ponnambalam,et al.  A GA-SA Multiobjective Hybrid Search Algorithm for Integrating Lot Sizing and Sequencing in Flow-Line Scheduling , 2003 .

[5]  Lorenzo Mussone,et al.  NEURAL-NETWORK MODELS FOR CLASSIFICATION AND FORECASTING OF FREEWAY TRAFFIC FLOW STABILITY , 1994 .

[6]  Ping-Feng Pai,et al.  A recurrent support vector regression model in rainfall forecasting , 2007 .

[7]  Billy M. Williams,et al.  MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS , 1999 .

[8]  Emile H. L. Aarts,et al.  Global optimization and simulated annealing , 1991, Math. Program..

[9]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[10]  Mark Dougherty,et al.  SHOULD WE USE NEURAL NETWORKS OR STATISTICAL MODELS FOR SHORT TERM MOTORWAY TRAFFIC FORECASTING , 1997 .

[11]  Billy M. Williams.  Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling , 2001 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[14]  Corinne Ledoux,et al.  An urban traffic flow model integrating neural networks , 1997 .

[15]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[16]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[17]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[18]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[19]  Wei-Chiang Hong,et al.  An Improved Neural Network Model in Forecasting Arrivals , 2005 .

[20]  Eleni I. Vlahogianni,et al.  Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach , 2005 .

[21]  Oscar Cordón,et al.  A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems , 2002, Soft Comput..

[22]  R. C. Peralta,et al.  Closure to discussion on Optimal in-situ bioremediation design by hybrid genetic algorithm-simulated annealing , 2005 .

[23]  M. C. Jones,et al.  Spline Smoothing and Nonparametric Regression. , 1989 .

[24]  Bart van Arem,et al.  TRAVEL TIME ESTIMATION IN THE GERDIEN PROJECT , 1997 .

[25]  Ping-Feng Pai,et al.  Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..

[26]  Yiannis Kamarianakis,et al.  Space-time modeling of traffic flow , 2002, Comput. Geosci..

[27]  Mahmudur Rahman,et al.  Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing , 2004 .

[28]  Zongben Xu,et al.  Three improved neural network models for air quality forecasting , 2003 .

[29]  Fang Zhao,et al.  Simulated Annealing-Genetic Algorithm for Transit Network Optimization , 2006 .

[30]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[31]  Glen E. Johnson,et al.  Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation , 1993, Comput. Aided Des..

[32]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[33]  M. Punniyamoorthy,et al.  Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing , 2005 .

[34]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[35]  Ping-Feng Pai,et al.  Using support vector machines to forecast the production values of the machinery industry in Taiwan , 2005 .

[36]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

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

[38]  P C Vythoulkas,et al.  ALTERNATIVE APPROACHES TO SHORT TERM TRAFFIC FORECASTING FOR USE IN DRIVER INFORMATION SYSTEMS , 1993 .

[39]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[40]  Cliff T. Ragsdale,et al.  A Simulated Annealing Genetic Algorithm for the Electrical Power Districting Problem , 2003, Ann. Oper. Res..

[41]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[42]  Ping-Feng Pai,et al.  Potential assessment of the support vector regression technique in rainfall forecasting , 2007 .

[43]  Stephen F. Witt,et al.  Forecasting tourism demand: A comparison of the accuracy of several quantitative methods , 1989 .

[44]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[45]  Wei-Chiang Hong.  The Application of Support Vector Machines to Forecast Tourist Arrivals in Barbados: An Empirical Study , 2006 .

[46]  Joe Whittaker,et al.  TRACKING AND PREDICTING A NETWORK TRAFFIC PROCESS , 1997 .

[47]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[48]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[49]  Lorenzo Mussone,et al.  A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting , 2001, Neural Computing & Applications.

[50]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[51]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[52]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[53]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .