Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

Bin Xia; Fan Yu Kong; Song Yuan Xie

doi:10.4028/www.scientific.net/AMM.433-435.612

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 433-435Passenger Flow Forecast of Urban Rail Transit...

Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

Abstract:

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

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Periodical:

Applied Mechanics and Materials (Volumes 433-435)

Pages:

612-616

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.433-435.612

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Online since:

October 2013

Authors:

Bin Xia, Fan Yu Kong, Song Yuan Xie

Keywords:

Forecast, Passenger Flow, Support Vector Regression (SVR), Urban Rail Transit

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References

[1] Wu Qiang, etc. The Application of Grey Forecasting Method in urban rail transit passenger flow forecasting. Study on Urban Rail Transit [J ], 2004, 7(3): 52-55.

DOI: 10.1061/9780784484265.107

Google Scholar

[2] Wang Yanhui, etc. Railway Passenger Traffic Volume Data Mining Forecasting Method and Its Application. Journal of Railway [J ], 2004, 26(5): 1-7.

Google Scholar

[3] Ming-Wei Chang，Chih-Jen Lin. Leave-one-out Bounds for Support Vector Regression Model Selection [J]. Neural Computation, 2005, 17(5): 1188-1222.

DOI: 10.1162/0899766053491869

Google Scholar

[4] Kyoung jae Kim. Financial time series forecasting using support vector machines [J]. Neurocomputing, 2003 , 55 (3): 307-319.

DOI: 10.1016/s0925-2312(03)00372-2

Google Scholar

[5] Chang, C. -C. and Chih-Jen Lin (2001). LIBSVM: a library for support vector machines. Software available at http: /www. csie. ntu. edu. tw/~cjlin/libsvm.

Google Scholar