Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 2
Abstract
Holiday passenger flow forecasting is essential to transportation plan-making and passenger flow organization in metro systems during holidays. Usually, daily passenger flow characteristics show a great difference between holidays and normal days, and the annual growth of holiday passenger flow seems more complicated. Least-square support vector machine (LSSVM) is able to handle the complex fluctuations in holiday daily passenger flow, but it suffers from critical parameter selection, and sparseness is also lost in the LSSVM solution. In an attempt to forecast holiday passenger flow accurately, this paper proposes an approach based on the modified LSSVM, in which an improved particle-swarm optimization (IPSO) algorithm is developed to optimize parameters and pruning algorithm is used to achieve sparseness, as well as a new evaluation indicator based on the -fold cross-validation method to evaluate the training performance. Finally, passenger flow data for Guangzhou Metro stations in China during the National Day holiday from 2011 to 2014 are applied as numerical examples to validate the performance of the proposed approach. The results show that the modified LSSVM model is an effective forecasting approach with higher accuracy than other alternative models.
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Acknowledgments
This research is supported by the Fundamental Research Funds for the Central Universities (No. 2016YJS079).
References
Cao, L. J., and Tay, F. E. (2003). “Support vector machine with adaptive parameters in financial time series forecasting.” IEEE Trans. Neural Networks, 14(6), 1506–1518.
Chen, Q., Li, W., and Zhao, J. (2011). “The use of LS-SVM for short-term passenger flow prediction.” Transport, 26(1), 5–10.
Chen, R., Liang, C., Hong, W., and Gu, D. (2015). “Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm.” Appl. Soft Comput., 26, 435–443.
Clark, S. (2003). “Traffic prediction using multivariate nonparametric regression.” J. Transp. Eng., 161–168.
Du, B., Lee, J., Chien, S., Dimitrijevic, B., and Kim, K. (2015). “Short-term freeway work zone capacity estimation using support vector machine incorporated with probe-vehicle data.” Transportation Research Board 94th Annual Meeting (No. 15-4248), Transportation Research Board Committee, Washington, DC.
Guin, A. (2006). “Travel time prediction using a seasonal autoregressive integrated moving average time series model.” Intelligent Transportation Systems Conf., ITSC’06, IEEE, New York, 493–498.
Kennedy, J. (2010). “Particle swarm optimization.” Encyclopedia of machine learning, Springer, New York, 760–766.
Lee, S., and Fambro, D. (1999). “Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting.” Transp. Res. Rec., 1678, 179–188.
Lim, C., and McAleer, M. (2002). “Time series forecast of international travel demand for Australia.” Tourism Manage., 23(4), 389–396.
Long, B., Xian, W., Li, M., and Wang, H. (2014). “Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance.” Neurocomputing, 133, 237–248.
Ngoduy, D. (2008). “Applicable filtering framework for online multiclass freeway network estimation.” Phys. A: Stat. Mech. Appl., 387(2–3), 599–616.
Smith, B. L., and Demetsky, M. J. (1994). “Short-term traffic flow prediction: Neural network approach.” Transp. Res. Rec., 1453, 98–104.
Smith, B. L., and Demetsky, M. J. (1997). “Traffic flow forecasting: Comparison of modeling approaches.” J. Transp. Eng., 261–266.
Smith, B. L., Williams, B. M., and Keith Oswald, R. (2002). “Comparison of parametric and nonparametric models for traffic flow forecasting.” Transp. Res. Part C. Emerging Technol., 10(4), 303–321.
Suykens, J. A., De Brabanter, J., Lukas, L., and Vandewalle, J. (2002). “Weighted least squares support vector machines: Robustness and sparse approximation.” Neurocomputing, 48(1), 85–105.
Suykens, J. A., Lukas, L., and Vandewalle, J. (2000). “Sparse approximation using least squares support vector machines.” Proc., IEEE Int. Symp. on Circuits and Systems, Vol. 2, IEEE, New York, 757–760.
Tang, Y. F., Lam, W. H. K., Ng, P. L. P. (2003). “Comparison of four modeling techniques for short-term AADT forecasting in Hong Kong.” J. Transp. Eng., 271–277.
Vapnik, V. (1995). The nature of statistical learning theory, Springer, Berlin.
Vlahogianni, E. I., Karlaftis, M. G., and Golias, J. C. (2014). “Short-term traffic forecasting: Where we are and where we’re going.” Transp. Res. Part C. Emerging Technol., 43, 3–19.
Wei, Y., and Chen, M. (2012). “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks.” Transp. Res. Part C. Emerging Technol., 21(1), 148–162.
Williams, B., Durvasula, P., and Brown, D. (1998). “Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models.” Transp. Res. Rec., 1644, 132–141.
Williams, B. M., and Hoel, L. A. (2003). “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results.” J. Transp. Eng., 664–672.
Xie, Y., Zhao, K., Sun, Y., and Chen, D. (2010). “Gaussian processes for short-term traffic volume forecasting.” Transp. Res. Rec., 2165, 69–78.
Xu, H., and Chen, G. (2013). “An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO.” Mech. Syst. Signal Process., 35(1-2), 167–175.
Yang, J., and Hou, Z. (2013). “A wavelet analysis based on LSSVM rail transit passenger flow prediction method.” China Railway Sci., 34(3), 122–126.
Zhang, N., Zhang, Y., and Lu, H. (2011). “Seasonal autoregressive integrated moving average and support vector machine models: Prediction of short-term traffic flow on freeways.” Transp. Res. Rec., 2215, 85–92.
Zhang, Y., and Xie, Y. (2008). “Forecasting of short-term freeway volume with v-support vector machines.” Transp. Res. Rec., 2024, 92–99.
Zheng, W., Lee, D. H., and Shi, Q. (2006). “Short-term freeway traffic flow prediction: Bayesian combined neural network approach.” J. Transp. Eng., 114–121.
Koga T. (1981). “Motion-compensated interframe coding for video conferencing.” Proc., NTC 81, C9-6 (in Chinese).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 2 • February 2017
Copyright
©2016 American Society of Civil Engineers.
History
Received: Jan 10, 2016
Accepted: Sep 15, 2016
Published online: Nov 17, 2016
Published in print: Feb 1, 2017
Discussion open until: Apr 17, 2017
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