An adaptive-margin support vector regression for short-term traffic flow forecast

Journal of Intelligent Transportation Systems: Technology, Planning, and Operations

Volumn 17, Issue 4, 2013, Pages 317-327

  Wei, Dali ^a   Liu, Hongchao ^a  

^a Texas Tech University   (United States)

Author keywords

margin; Day to Day Traffic Variation; Short Term Traffic Forecasting; Support Vector Regression

Indexed keywords

COMPARATIVE EXPERIMENTS; COMPLEX CHARACTERISTICS; DAY-TO-DAY TRAFFIC VARIATION; HEURISTIC INFORMATION; SHORT-TERM TRAFFIC FLOW; SHORT-TERM TRAFFIC FORECASTING; STOCHASTIC NOISE; SUPPORT VECTOR REGRESSION (SVR);

REGRESSION ANALYSIS;

TRAFFIC CONTROL;

EXPERIMENTAL STUDY; FORECASTING METHOD; HEURISTICS; NONLINEARITY; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; TEMPORAL VARIATION; TRAFFIC MANAGEMENT;

EID: 84889769137     PISSN: 15472450     EISSN: 15472442     Source Type: Journal    
DOI: 10.1080/15472450.2013.771107     Document Type: Article

References (27)

1. Abdy, Z. R.,&Hellinga, B. R. (2008).Use of microsimulation to model day-to-day variability of intersection performance. Transportation Research Record: Journal of the Transportation Research Board, 2088(1), 18-25.
2. Canu, S., Grandvalet, Y., Guigue, V., & Rakotomamonjy, A. (2005). SVM and kernel methods Matlab toolbox. Perception Systemes et Information, INSA de Rouen, Rouen, France, 2, 2.
3. Castro-Neto, M., Jeong, Y. S., Jeong, M. K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), 6164-6173.
4. Chen, F., Wei, D., & Tang, Y. (2010). Wavelet analysis based sparse LS-SVR for time series data. Paper presented at the 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), February 2011.
5. Chen, F., Wei, D., & Tang, Y. (2011). Virtual ion selective electrode for online measurement of nutrient solution components. Sensors Journal, IEEE, 11(2), 462-468.
6. Chen, S., Hong, X., Harris, C. J., & Sharkey, P. M. (2004). Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(2), 898-911.
7. Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17(1), 113-126. doi:10.1016/s0893-6080(03)00169-2
8. Davis, G. A., & Nihan, N. L. (1991). Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117(2), 178-188.
9. Hellinga, B., & Abdy, Z. (2008). Signalized intersection analysis and design: Implications of day-to-day variability in peak-hour volumes on delay. Journal of Transportation Engineering, 134(7), 307-318.
10. Jiang, X., & Adeli, H. (2005). Dynamic wavelet neural network model for traffic flow forecasting. Journal of Transportation Engineering, 131(10), 771-779.
11. Levinson, H. S., Sullivan, D., & Bryson, R. W. (2006). Effects of urban traffic volume variations on service levels. Paper presented at the Transportation Research Board 85th Annual Meeting, January 2006.
12. Li, J. Q. (2011). Discretization modeling, integer programming formulations and dynamic programming algorithms for robust traffic signal timing. Transportation Research Part C: Emerging Technologies, 19(4), 708-719.
13. Li, L., Lin,W.,&Liu, H. (2006). Type-2 fuzzy logic approach for shortterm traffic forecasting. Paper presented at the Intelligent Transport Systems, IEE Proceedings, March 2006.
14. Park, B., Messer, C. J., & Urbanik, T. II. (1998). Short-term freeway traffic volume forecasting using radial basis function neural network. Transportation Research Record: Journal of the Transportation Research Board, 1651(1), 39-47.
15. Rakha, H., & Van Aerde, M. (1995). Statistical analysis of day-to-day variations in real-time traffic flow data. Transportation Research Record, 1510, 26-34.
16. Sherrod, P. H. (2003). DTREG predictive modeling software. Available at http://www.dtreg.com
17. Smith, B. L., Williams, B. M., & Oswald, K. R. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303-321.
18. Smola, A. J., Schölkopf, B., & Müller, K. R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11(4), 637-649.
19. Vapnik, V. (1995). The nature of statistical learning theory. NewYork, NY: Springer-Verlag.
20. Wei, D., Chen, F., & Chen, C. (2010). Least-square waveletsupport vector machine for short-term traffic flow forecast. Mediterranean Journal of Measurement and Control, 6(2), 39-45.
21. Williams, B. M., Durvasula, P. K., & Brown, D. E. (1998). Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record: Journal of the Transportation Research Board, 1644(1), 132-141.
22. Williams, B. M., & Hoel, L. A. (1999). Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process. Doctoral dissertation. Department of Civil Engineering, University of Virginia, Charlottesville, Virginia.
23. Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664-672.
24. Xie, Y., & Zhang, Y. (2006). A wavelet network model for shortterm traffic volume forecasting. Journal of Intelligent Transportation Systems, 10(3), 141-150.
25. Yin, Y. (2008). Robust optimal traffic signal timing. Transportation Research Part B: Methodological, 42(10), 911-924.
26. Zhang, L., Yin, Y., & Lou, Y. (2010). Robust signal timing for arterials under day-to-day demand variations. Transportation Research Record: Journal of the Transportation Research Board, 2192(1), 156-166.
27. Zhang, Y., & Xie, Y. (2008). Forecasting of short-term freeway volume with v-support vector machines. Transportation Research Record: Journal of the Transportation Research Board, 2024(1), 92-99.