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Volumn 217, Issue 15, 2011, Pages 6733-6747

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

Author keywords

Back propagation neural network BPNN; Holt Winters (HW); Hybrid evolutionary algorithms; Hybrid genetic algorithm simulated annealing algorithm (GA SA); SARIMA; Seasonal Holt Winters (SHW); Support vector regression; Traffic flow forecasting

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; HOLT-WINTERS; HOLT-WINTERS (HW); HYBRID EVOLUTIONARY ALGORITHM; HYBRID GENETIC ALGORITHM-SIMULATED ANNEALING ALGORITHM (GA-SA); SARIMA; SUPPORT VECTOR REGRESSION; TRAFFIC FLOW FORECASTING;

EID: 79952362137     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2011.01.073     Document Type: Article
Times cited : (121)

References (64)
  • 3
    • 0037954189 scopus 로고    scopus 로고
    • A multivariate state space approach for urban traffic flow modeling and prediction
    • DOI 10.1016/S0968-090X(03)00004-4, PII S0968090X03000044
    • A. Stathopoulos, and G.M. Karlaftis A multivariate state space approach for urban traffic flow modeling and prediction Transportation Research Part C 11 2003 121 135 (Pubitemid 36594926)
    • (2003) Transportation Research Part C: Emerging Technologies , vol.11 , Issue.2 , pp. 121-135
    • Stathopoulos, A.1    Karlaftis, M.G.2
  • 6
    • 0003255680 scopus 로고
    • Alternative approaches to short term forecasting for use in driver information systems
    • P.C. Vythoulkas Alternative approaches to short term forecasting for use in driver information systems C.F. Daganzo, Transportation and Traffic Theory 1993 Elsevier Amsterdam
    • (1993) Transportation and Traffic Theory
    • Vythoulkas, P.C.1
  • 7
    • 0001845905 scopus 로고
    • ATHENA: A method for short-term inter-urban motorway traffic forecasting
    • M. Danech-Pajouh, and M. Aron ATHENA: a method for short-term inter-urban motorway traffic forecasting Recherche Transports Sécurité 6 1991 11 16
    • (1991) Recherche Transports Sécurité , vol.6 , pp. 11-16
    • Danech-Pajouh, M.1    Aron, M.2
  • 10
    • 0036692982 scopus 로고    scopus 로고
    • Comparison of parametric and nonparametric models for traffic flow forecasting
    • B.L. Smith, B.M. Williams, and R.K. Oswald Comparison of parametric and nonparametric models for traffic flow forecasting Transportation Research Part C 10 2002 303 321
    • (2002) Transportation Research Part C , vol.10 , pp. 303-321
    • Smith, B.L.1    Williams, B.M.2    Oswald, R.K.3
  • 13
    • 12344314263 scopus 로고    scopus 로고
    • Space-time modeling of traffic flow
    • DOI 10.1016/j.cageo.2004.05.012, PII S0098300404001888, Geospatial Research in Europe: AGILE 2003
    • Y. Kamarianakis, and P. Prastacos Space-time modeling of traffic flow Computers & Geosciences 31 2005 119 133 (Pubitemid 40133572)
    • (2005) Computers and Geosciences , vol.31 , Issue.2 , pp. 119-133
    • Kamarianakis, Y.1    Prastacos, P.2
  • 14
    • 0031092111 scopus 로고    scopus 로고
    • Should we use neural networks or statistical models for short-term motorway traffic forecasting?
    • PII S0169207096006991
    • H.R. Kirby, S.M. Watson, and M.S. Dougherty Should we use neural networks or statistical models for short-term motorway traffic forecasting? International Journal of Forecasting 13 1997 43 50 (Pubitemid 127165543)
    • (1997) International Journal of Forecasting , vol.13 , Issue.1 , pp. 43-50
    • Kirby, H.R.1    Watson, S.M.2    Dougherty, M.S.3
  • 15
    • 0035563672 scopus 로고    scopus 로고
    • Multivariate vehicular traffic flow prediction: Evaluation of ARIMAX modeling
    • 01-3488
    • B.M. Williams Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling Transportation Research Record 1776 2001 194 200 (Pubitemid 34404280)
    • (2001) Transportation Research Record , Issue.1776 , pp. 194-200
    • Williams, B.M.1
  • 18
    • 0035567818 scopus 로고    scopus 로고
    • A study of hybrid neural network approaches and the effects of missing data on traffic forecasting
    • H. Chen, S. Grant-Muller, L. Mussone, and F. Montgomery A study of hybrid neural network approaches and the effects of missing data on traffic forecasting Neural Computing and Applications 10 2001 277 286 (Pubitemid 33725833)
    • (2001) Neural Computing and Applications , vol.10 , Issue.3 , pp. 277-286
    • Chen, H.1    Grant-Muller, S.2    Mussone, L.3    Montgomery, F.4
  • 19
    • 0031092866 scopus 로고    scopus 로고
    • Short-term inter-urban traffic forecasts using neural networks
    • PII S0169207096006978
    • M.S. Dougherty, and M.R. Cobbett Short-term inter-urban traffic forecasts using neural networks International Journal of Forecasting 13 1997 21 31 (Pubitemid 127165541)
    • (1997) International Journal of Forecasting , vol.13 , Issue.1 , pp. 21-31
    • Dougherty, M.S.1    Cobbett, M.R.2
  • 20
    • 0030081052 scopus 로고    scopus 로고
    • Neural-network models for classification and forecasting of freeway traffic flow stability
    • DOI 10.1016/0967-0661(95)00221-9, PII S0967066195002219
    • L. Florio, and L. Mussone Neural network models for classification and forecasting of freeway traffic flow stability Control Engineering Practice 4 1996 153 164 (Pubitemid 126399839)
    • (1996) Control Engineering Practice , vol.4 , Issue.2 , pp. 153-164
    • Florio, L.1    Mussone, L.2
  • 21
    • 0031284302 scopus 로고    scopus 로고
    • An urban traffic flow model integrating neural networks
    • C. Ledoux An urban traffic flow model integrating neural networks Transportation Research Part C 5 1997 287 300
    • (1997) Transportation Research Part C , vol.5 , pp. 287-300
    • Ledoux, C.1
  • 22
    • 23844513726 scopus 로고    scopus 로고
    • Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
    • DOI 10.1016/j.trc.2005.04.007, PII S0968090X05000276
    • E.I. Vlahogianni, M.G. Karlaftis, and J.C. Golias Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach Transportation Research Part C 13 2005 211 234 (Pubitemid 41179185)
    • (2005) Transportation Research Part C: Emerging Technologies , vol.13 , Issue.3 , pp. 211-234
    • Vlahogianni, E.I.1    Karlaftis, M.G.2    Golias, J.C.3
  • 23
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: The state of the art
    • PII S0169207097000447
    • G. Zhang, B.E. Patuwo, and M.Y. Hu Forecasting with artificial neural networks: the state of art International Journal of Forecasting 14 1998 35 62 (Pubitemid 128340470)
    • (1998) International Journal of Forecasting , vol.14 , Issue.1 , pp. 35-62
    • Zhang, G.1    Eddy Patuwo, B.2    Y. Hu, M.3
  • 26
    • 34249753618 scopus 로고
    • Support vector networks
    • C. Cortes, and V. Vapnik Support vector networks Machine Learning 20 1995 273 297
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 27
    • 84887252594 scopus 로고    scopus 로고
    • Support vector machine for function approximation, regression estimation, and signal processing
    • V. Vapnik, S. Golowich, and A. Smola Support vector machine for function approximation, regression estimation, and signal processing Advances in Neural Information Processing Systems 9 1996 281 287
    • (1996) Advances in Neural Information Processing Systems , vol.9 , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.3
  • 29
    • 0002562734 scopus 로고
    • Forecasting tourism demand: A comparison of accuracy of several quantitative methods
    • C.A. Martin, and S.F. Witt Forecasting tourism demand: a comparison of accuracy of several quantitative methods International Journal of Forecasting 5 1989 7 19
    • (1989) International Journal of Forecasting , vol.5 , pp. 7-19
    • Martin, C.A.1    Witt, S.F.2
  • 30
    • 0037381038 scopus 로고    scopus 로고
    • Support vector machines experts for time series forecasting
    • DOI 10.1016/S0925-2312(02)00577-5, PII S0925231202005775
    • L. Cao Support vector machines experts for time series forecasting Neurocomputing 51 2003 321 339 (Pubitemid 36367241)
    • (2003) Neurocomputing , vol.51 , pp. 321-339
    • Cao, L.1
  • 31
    • 84863268449 scopus 로고    scopus 로고
    • Dynamic support vector machines for non-stationary time series forecasting
    • L. Cao, and Q. Gu Dynamic support vector machines for non-stationary time series forecasting Intelligent Data Analysis 6 2002 67 83
    • (2002) Intelligent Data Analysis , vol.6 , pp. 67-83
    • Cao, L.1    Gu, Q.2
  • 32
    • 13544267510 scopus 로고    scopus 로고
    • Forecasting stock market movement direction with support vector machine
    • DOI 10.1016/j.cor.2004.03.016, PII S0305054804000681, Application of Neural Networks
    • W. Huang, Y. Nakamori, and S.Y. Wang Forecasting stock market movement direction with support vector machine Computers & Operations Research 32 2005 2513 2522 (Pubitemid 40219758)
    • (2005) Computers and Operations Research , vol.32 , Issue.10 , pp. 2513-2522
    • Huang, W.1    Nakamori, Y.2    Wang, S.-Y.3
  • 33
    • 27944432882 scopus 로고    scopus 로고
    • Using support vector machines in forecasting production values of machinery industry in Taiwan
    • P.F. Pai, and C.S. Lin Using support vector machines in forecasting production values of machinery industry in Taiwan International Journal of Advanced Manufacturing Technology 27 2005 205 210
    • (2005) International Journal of Advanced Manufacturing Technology , vol.27 , pp. 205-210
    • Pai, P.F.1    Lin, C.S.2
  • 34
    • 20344388265 scopus 로고    scopus 로고
    • A hybrid ARIMA and support vector machines model in stock price forecasting
    • P.F. Pai, and C.S. Lin A hybrid ARIMA and support vector machines model in stock price forecasting Omega 33 2005 497 505
    • (2005) Omega , vol.33 , pp. 497-505
    • Pai, P.F.1    Lin, C.S.2
  • 35
    • 0001023715 scopus 로고    scopus 로고
    • Application of support vector machines in financial time series forecasting
    • DOI 10.1016/S0305-0483(01)00026-3, PII S0305048301000263
    • F.E.H. Tay, and L. Cao Application of support vector machines in financial time series forecasting Omega 29 2001 309 317 (Pubitemid 33628757)
    • (2001) Omega , vol.29 , Issue.4 , pp. 309-317
    • Tay, F.E.H.1    Cao, L.2
  • 36
    • 0036825901 scopus 로고    scopus 로고
    • Modified support vector machines in financial time series forecasting
    • DOI 10.1016/S0925-2312(01)00676-2, PII S0925231201006762
    • F.E.H. Tay, and L. Cao Modified support vector machines in financial time series forecasting Neurocomputing 48 2002 847 861 (Pubitemid 36221530)
    • (2002) Neurocomputing , vol.48 , pp. 847-861
    • Tay, F.E.H.1    Cao, L.J.2
  • 37
    • 33646130599 scopus 로고    scopus 로고
    • Software reliability forecasting by support vector machines with simulated annealing algorithms
    • P.F. Pai, and W.C. Hong Software reliability forecasting by support vector machines with simulated annealing algorithms Journal of Systems and Software 79 2006 747 755
    • (2006) Journal of Systems and Software , vol.79 , pp. 747-755
    • Pai, P.F.1    Hong, W.C.2
  • 38
    • 33846428782 scopus 로고    scopus 로고
    • Potential assessment of the support vector regression technique in rainfall forecasting
    • W.C. Hong, and P.F. Pai Potential assessment of the support vector regression technique in rainfall forecasting Water Resources Management 21 2007 495 513
    • (2007) Water Resources Management , vol.21 , pp. 495-513
    • Hong, W.C.1    Pai, P.F.2
  • 39
    • 0442296729 scopus 로고    scopus 로고
    • Support vector machines for wind speed prediction
    • DOI 10.1016/j.renene.2003.11.009
    • M.A. Mohandes, T.O. Halawani, S. Rehman, and A.A. Hussain Support vector machines for wind speed prediction Renewable Energy 29 2004 939 947 (Pubitemid 38242182)
    • (2004) Renewable Energy , vol.29 , Issue.6 , pp. 939-947
    • Mohandes, M.A.1    Halawani, T.O.2    Rehman, S.3    Hussain, A.A.4
  • 40
    • 33947537723 scopus 로고    scopus 로고
    • A recurrent support vector regression model in rainfall forecasting
    • P.F. Pai, and W.C. Hong A recurrent support vector regression model in rainfall forecasting Hydrological Processes 21 2007 819 827
    • (2007) Hydrological Processes , vol.21 , pp. 819-827
    • Pai, P.F.1    Hong, W.C.2
  • 41
    • 0037255890 scopus 로고    scopus 로고
    • Three improved neural network models for air quality forecasting
    • W. Wang, Z. Xu, and J.W. Lu Three improved neural network models for air quality forecasting Engineering Computations 20 2003 192 210
    • (2003) Engineering Computations , vol.20 , pp. 192-210
    • Wang, W.1    Xu, Z.2    Lu, J.W.3
  • 42
    • 18144394762 scopus 로고    scopus 로고
    • Forecasting regional electric load based on recurrent support vector machines with genetic algorithms
    • P.F. Pai, and W.C. Hong Forecasting regional electric load based on recurrent support vector machines with genetic algorithms Electric Power Systems Research 74 2005 417 425
    • (2005) Electric Power Systems Research , vol.74 , pp. 417-425
    • Pai, P.F.1    Hong, W.C.2
  • 43
    • 18544377981 scopus 로고    scopus 로고
    • Support vector machines with simulated annealing algorithms in electricity load forecasting
    • P.F. Pai, and W.C. Hong Support vector machines with simulated annealing algorithms in electricity load forecasting Energy Conversion and Management 46 2005 2626 2669
    • (2005) Energy Conversion and Management , vol.46 , pp. 2626-2669
    • Pai, P.F.1    Hong, W.C.2
  • 44
    • 27744588529 scopus 로고    scopus 로고
    • An improved neural network model in forecasting arrivals
    • P.F. Pai, and W.C. Hong An improved neural network model in forecasting arrivals Annals of Tourism Research 32 2005 1138 1141
    • (2005) Annals of Tourism Research , vol.32 , pp. 1138-1141
    • Pai, P.F.1    Hong, W.C.2
  • 45
    • 36549006345 scopus 로고    scopus 로고
    • The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study
    • P.F. Pai, W.C. Hong, P.T. Chang, and C.T. Chen The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study International Journal of Management 23 2006 375 385
    • (2006) International Journal of Management , vol.23 , pp. 375-385
    • Pai, P.F.1    Hong, W.C.2    Chang, P.T.3    Chen, C.T.4
  • 46
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • DOI 10.1016/S0893-6080(03)00169-2
    • V. Cherkassky, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Networks 17 2004 113 126 (Pubitemid 38019003)
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 48
  • 49
    • 0027667217 scopus 로고
    • Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation
    • J. Lee, and G.E. Johnson Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation Computer Aided Design 25 1983 601 611
    • (1983) Computer Aided Design , vol.25 , pp. 601-611
    • Lee, J.1    Johnson, G.E.2
  • 50
    • 11844250090 scopus 로고    scopus 로고
    • Optimal in situ bioremediation design by hybrid genetic algorithm-simulated annealing
    • DOI 10.1061/(ASCE)0733-9496(2005)131:1(67)
    • H.J. Shieh, and R.C. Peralta Optimal in situ bioremediation design by hybrid genetic algorithm-simulated annealing Journal of Water Resources Planning and Management 131 2005 67 78 (Pubitemid 40106708)
    • (2005) Journal of Water Resources Planning and Management , vol.131 , Issue.1 , pp. 67-78
    • Shieh, H.-J.1    Peralta, R.C.2
  • 51
    • 0037282048 scopus 로고    scopus 로고
    • A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling
    • S.G. Ponnambalam, and M.M. Reddy A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling International Journal of Advanced Manufacturing Technology 21 2003 126 137
    • (2003) International Journal of Advanced Manufacturing Technology , vol.21 , pp. 126-137
    • Ponnambalam, S.G.1    Reddy, M.M.2
  • 52
    • 33645530240 scopus 로고    scopus 로고
    • Simulated annealing - Genetic algorithm for transit network optimization
    • F. Zhao, and X. Zeng Simulated annealing - genetic algorithm for transit network optimization Journal of Computing in Civil Engineering 20 2006 57 68
    • (2006) Journal of Computing in Civil Engineering , vol.20 , pp. 57-68
    • Zhao, F.1    Zeng, X.2
  • 53
    • 0012586289 scopus 로고    scopus 로고
    • A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems
    • O. Cordón, F. Moya, and C. Zarco A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems Soft Computing 6 2002 308 319
    • (2002) Soft Computing , vol.6 , pp. 308-319
    • Cordón, O.1    Moya, F.2    Zarco, C.3
  • 54
    • 22044438294 scopus 로고    scopus 로고
    • Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing
    • DOI 10.1007/s00170-003-1976-4
    • K. Ganesh, and M. Punniyamoorthy Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing International Journal of Advanced Manufacturing Technology 26 2005 148 154 (Pubitemid 40963676)
    • (2005) International Journal of Advanced Manufacturing Technology , vol.26 , Issue.1-2 , pp. 148-154
    • Ganesh, K.1    Punniyamoorthy, M.2
  • 56
    • 0037278595 scopus 로고    scopus 로고
    • A simulated annealing genetic algorithm for the electrical power districting problem
    • P.K. Bergey, C.T. Ragsdale, and M. Hoskote A simulated annealing genetic algorithm for the electrical power districting problem Annals of Operations Research 121 2003 33 55
    • (2003) Annals of Operations Research , vol.121 , pp. 33-55
    • Bergey, P.K.1    Ragsdale, C.T.2    Hoskote, M.3
  • 58
    • 0000082693 scopus 로고
    • Forecasting sales by exponentially weighted moving averages
    • P.R. Winters Forecasting sales by exponentially weighted moving averages Management Science 6 1960 324 342
    • (1960) Management Science , vol.6 , pp. 324-342
    • Winters, P.R.1
  • 61
    • 0032786569 scopus 로고    scopus 로고
    • Improving support vector machine classifiers by modifying kernel functions
    • DOI 10.1016/S0893-6080(99)00032-5, PII S0893608099000325
    • S. Amari, and S. Wu Improving support vector machine classifiers by modifying kernel functions Neural Networks 12 1999 783 789 (Pubitemid 29359218)
    • (1999) Neural Networks , vol.12 , Issue.6 , pp. 783-789
    • Amari, S.1    Wu, S.2


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