메뉴 건너뛰기




Volumn , Issue 2165, 2010, Pages 69-78

Gaussian processes for short-term traffic volume forecasting

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); HIGHWAY ENGINEERING; HIGHWAY TRAFFIC CONTROL; INTELLIGENT SYSTEMS; TIME SERIES ANALYSIS; TRAVEL TIME;

EID: 78651316181     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2165-08     Document Type: Article
Times cited : (112)

References (27)
  • 1
    • 0032207514 scopus 로고    scopus 로고
    • Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models
    • TRB, National Research Council, Washington, D.C.
    • Williams, B. M., P. K. Durvasula, and D. E. Brown. Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models. In Transportation Research Record 1644, TRB, National Research Council, Washington, D.C., 1998, pp. 132-141.
    • (1998) Transportation Research Record 1644 , pp. 132-141
    • Williams, B.M.1    Durvasula, P.K.2    Brown, D.E.3
  • 2
    • 0018729076 scopus 로고
    • Analysis of freeway traffic time-series data by using box-jenkins techniques
    • TRB, National Research Council, Washington, D.C.
    • Ahmed, M. S., and A. R. Cook. Analysis of Freeway Traffic Time-Series Data By Using Box-Jenkins Techniques. In Transportation Research Record 722, TRB, National Research Council, Washington, D.C., 1979, pp. 1-9.
    • (1979) Transportation Research Record 722 , pp. 1-9
    • Ahmed, M.S.1    Cook, A.R.2
  • 3
    • 0019025588 scopus 로고
    • Use of the box and jenkins time series technique in traffic forecasting
    • Nihan, N. L., and K. O. Holmesland. Use of the Box and Jenkins Time Series Technique in Traffic Forecasting. Transportation, Vol. 9, No. 2, 1980, pp. 125-143.
    • (1980) Transportation , vol.9 , Issue.2 , pp. 125-143
    • Nihan, N.L.1    Holmesland, K.O.2
  • 4
    • 0026128928 scopus 로고
    • Nonparametric regression and short-term freeway traffic forecasting
    • Davis, G. A., and N. L. Nihan. Nonparametric Regression and Short-Term Freeway Traffic Forecasting. Journal of Transportation Engineering, Vol. 117, No. 2, 1991, pp. 178-188.
    • (1991) Journal of Transportation Engineering , vol.117 , Issue.2 , pp. 178-188
    • Davis, G.A.1    Nihan, N.L.2
  • 5
    • 0031472064 scopus 로고    scopus 로고
    • Traffic flow forecasting: Comparison of modeling approaches
    • Smith, B. L., and M. J. Demetsky. Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering, Vol. 123, No. 4, 1997, pp. 261-266.
    • (1997) Journal of Transportation Engineering , vol.123 , Issue.4 , pp. 261-266
    • Smith, B.L.1    Demetsky, M.J.2
  • 6
    • 0021375695 scopus 로고
    • Dynamic prediction of traffic volume through kalman filtering theory
    • Okutani, I., and Y. J. Stephanedes. Dynamic Prediction of Traffic Volume Through Kalman Filtering Theory. Transportation Research Part B, Vol. 18, No. 1, 1984, pp. 1-11.
    • (1984) Transportation Research Part B , vol.18 , Issue.1 , pp. 1-11
    • Okutani, I.1    Stephanedes, Y.J.2
  • 7
    • 0037954189 scopus 로고    scopus 로고
    • A multivariate state space approach for urban traffic flow modeling and prediction
    • Stathopoulos, A., and M. G. Karlaftis. A Multivariate State Space Approach for Urban Traffic Flow Modeling and Prediction. Transportation Research Part C, Vol. 11, No. 2, 2003, pp. 121-135.
    • (2003) Transportation Research Part C , vol.11 , Issue.2 , pp. 121-135
    • Stathopoulos, A.1    Karlaftis, M.G.2
  • 8
    • 34249030515 scopus 로고    scopus 로고
    • Short-term traffic volume forecasting using kalman filter with discrete wavelet decomposition
    • Xie, Y., Y. Zhang, and Z. Ye. Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition. Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 5, 2007, pp. 326-334.
    • (2007) Computer-Aided Civil and Infrastructure Engineering , vol.22 , Issue.5 , pp. 326-334
    • Xie, Y.1    Zhang, Y.2    Ye, Z.3
  • 9
    • 0001891123 scopus 로고
    • Short-term traffic flow prediction: Neural network approach
    • TRB, National Research Council, Washington, D.C.
    • Smith, B. L., and M. J. Demetsky. Short-Term Traffic Flow Prediction: Neural Network Approach. In Transportation Research Record 1453, TRB, National Research Council, Washington, D.C., 1994, pp. 98-104.
    • (1994) Transportation Research Record 1453 , pp. 98-104
    • Smith, B.L.1    Demetsky, M.J.2
  • 10
    • 0003023581 scopus 로고    scopus 로고
    • Short-term freeway traffic volume forecasting using radial basis function neural network
    • TRB, National Research Council, Washington, D.C.
    • Park, B., C. J. Messer, and T. Urbanik II. Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network. In Transportation Research Record 1651, TRB, National Research Council, Washington, D.C., 1998, pp. 39-47.
    • (1998) Transportation Research Record 1651 , pp. 39-47
    • Park, B.1    Messer, C.J.2    Urbanik, I.I.T.3
  • 11
    • 0036532655 scopus 로고    scopus 로고
    • Urban traffic flow prediction using a fuzzy-neural approach
    • Yin, H. B., S. C. Wong, J. M. Xu, and C. K. Wong. Urban Traffic Flow Prediction Using a Fuzzy-Neural Approach. Transportation Research Part C, Vol. 10, No. 2, 2002, pp. 85-98.
    • (2002) Transportation Research Part C , vol.10 , Issue.2 , pp. 85-98
    • Yin, H.B.1    Wong, S.C.2    Xu, J.M.3    Wong, C.K.4
  • 12
    • 33746860294 scopus 로고    scopus 로고
    • A wavelet network model for short-term traffic volume forecasting
    • DOI 10.1080/15472450600798551, PII J412T750H8Q20368
    • Xie, Y., and Y. Zhang. A Wavelet Network Model for Short-Term Traffic Volume Forecasting. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 10, No. 3, 2006, pp. 141-150. (Pubitemid 44180407)
    • (2006) Journal of Intelligent Transportation Systems: Technology, Planning, and Operations , vol.10 , Issue.3 , pp. 141-150
    • Xie, Y.1    Zhang, Y.2
  • 13
    • 33646762818 scopus 로고    scopus 로고
    • Accurate freeway travel time prediction with state-space neural networks under missing data
    • Van Lint, J. W. C., S. P. Hoogendoorn, and H. J. Van Zuylen. Accurate Freeway Travel Time Prediction with State-Space Neural Networks Under Missing Data. Transportation Research Part C, Vol. 13, Nos. 5-6, 2005, pp. 347-369.
    • (2005) Transportation Research Part C , vol.13 , Issue.5-6 , pp. 347-369
    • Van Lint, J.W.C.1    Hoogendoorn, S.P.2    Van Zuylen, H.J.3
  • 14
    • 0032623401 scopus 로고    scopus 로고
    • Forecasting freeway link travel times with a multilayer feedforward neural network
    • Park, D., and L. R. Rilett. Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network. Computer-Aided Civil and Infrastructure Engineering, Vol. 14, No. 5, 1999, pp. 357-367.
    • (1999) Computer-Aided Civil and Infrastructure Engineering , vol.14 , Issue.5 , pp. 357-367
    • Park, D.1    Rilett, L.R.2
  • 15
    • 23844513726 scopus 로고    scopus 로고
    • Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
    • Vlahogianni, E. I., 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, Vol. 13, No. 3, 2005, pp. 211-234.
    • (2005) Transportation Research Part C , vol.13 , Issue.3 , pp. 211-234
    • Vlahogianni, E.I.1    Karlaftis, M.G.2    Golias, J.C.3
  • 16
    • 40449104106 scopus 로고    scopus 로고
    • Forecasting of short-term freeway volume with v-support vector machines
    • Transportation Research Board of the National Academies, Washington, D.C.
    • Zhang, Y., and Y. Xie. Forecasting of Short-Term Freeway Volume with v-Support Vector Machines, In Transportation Research Record: Journal of the Transportation Research Board, No. 2024, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 92-99.
    • (2007) Transportation Research Record: Journal of the Transportation Research Board 2024 , pp. 92-99
    • Zhang, Y.1    Xie, Y.2
  • 18
    • 0030298951 scopus 로고    scopus 로고
    • Combining kohonen maps with ARIMA time series models to forecast traffic flow
    • Van Der Voort, M., M. Dougherty, and S. Watson. Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research Part C, Vol. 4, No. 5, 1996, pp. 307-318.
    • (1996) Transportation Research Part C , vol.4 , Issue.5 , pp. 307-318
    • Van Der Voort, M.1    Dougherty, M.2    Watson, S.3
  • 19
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik, K., M. Stinchcombe, and H. White. Multilayer Feedforward Networks Are Universal Approximators. Neural Networks, Vol. 2, No. 5, 1989, pp. 359-366.
    • (1989) Neural Networks , vol.2 , Issue.5 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 23
    • 12444291490 scopus 로고    scopus 로고
    • Gaussian processes for machine learning
    • Seeger, M. Gaussian Processes for Machine Learning. International Journal of Neural Systems, Vol. 14, No. 2, 2004, pp. 69-106.
    • (2004) International Journal of Neural Systems , vol.14 , Issue.2 , pp. 69-106
    • Seeger, M.1
  • 24
    • 0042326376 scopus 로고    scopus 로고
    • Bayesian trigonometric support vector classifier
    • Chu, W., S. S. Keerthi, and C. J. Ong. Bayesian Trigonometric Support Vector Classifier. Neural Computation, Vol. 15, No. 9, 2003, pp. 2227-2254.
    • (2003) Neural Computation , vol.15 , Issue.9 , pp. 2227-2254
    • Chu, W.1    Keerthi, S.S.2    Ong, C.J.3
  • 25
    • 55349107801 scopus 로고    scopus 로고
    • Bayesian learning with gaussian processes for supervised classification of hyperspectral data
    • Zhao, K., S. C. Popescu, and X. Zhang. Bayesian Learning with Gaussian Processes for Supervised Classification of Hyperspectral Data. Photogrammetric Engineering & Remote Sensing, Vol. 74, No. 10, 2008, pp. 1223-1234.
    • (2008) Photogrammetric Engineering & Remote Sensing , vol.74 , Issue.10 , pp. 1223-1234
    • Zhao, K.1    Popescu, S.C.2    Zhang, X.3
  • 26
    • 78651330230 scopus 로고    scopus 로고
    • Accessed Nov. 1, 2009
    • Documentation for GPML MATLAB Code. http://www.gaussian process.org/gpml/code/matlab/doc/. Accessed Nov. 1, 2009.
    • Documentation for GPML MATLAB Code
  • 27
    • 80055091440 scopus 로고    scopus 로고
    • (Version 2.9.1). Accessed July 3, 2009
    • The R Project for Statistical Computing (Version 2.9.1). http://www. r-project.org/. Accessed July 3, 2009.
    • The R Project for Statistical Computing


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.