01548816

Component

https://doi.org/10.3141/2443-03

http://scholar.google.com...+Liu&publication_year=2014

Traffic flow prediction is considered a key technology of intelligent transportation systems. This paper presents a hybrid model that combines double exponential smoothing (DES) and a support vector machine (SVM) to predict traffic flow patterns on the basis of weekly similarities in traffic flow. First, in the hybrid model, DES is applied to predict the future data, and its smoothing parameters are determined by the Levenberg-Marquardt algorithm. Second, the SVM is employed to estimate the residual series between the prediction results by the DES model and actual measured data. In the SVM model, the cross-correlation rule is used to optimize its parameters. Third, a case study to test the proposed model with the data at different temporal scales is presented. Furthermore, data-smoothing strategies, including difference and ratio schemes based on weekly similarities, are applied as data processes before prediction. The proposed hybrid model along with the processing scheme demonstrates superiority in prediction accuracy compared with autoregressive integrated moving average, DES, and DES-SVM models.

01548337

14-0644

English

0000-0002-4180-5628

pp 21-31

2014

Transportation Research Record: Journal of the Transportation Research Board

9780309295314

Print

Figures; References; Tables

Intelligent transportation systems; Mathematical prediction; Traffic data; Traffic flow

Support vector machines

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 24 2014 8:29AM

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