NDLI: Highway traffic forecasting by support vector regression model with tabu search algorithms

Content Provider	IEEE Xplore Digital Library
Author	Wei-Chiang Hong Ping-Feng Pai Shun-Lin Yang Theng, R.
Copyright Year	2006
Description	Author affiliation: Da-Yeh Univ., Changhua (Wei-Chiang Hong; Theng, R.)
Abstract	Due to the nonlinearity and seasonality of highway traffic, many advanced forecasting models have been designed to predict traffic flow. Recently, the support vector regression model (SVR) has been successfully applied in some nonlinear time series forecasting problems. This study develops a support vector regression model with tabu search algorithms (SVRTS) to predict monthly highway traffics in Taiwan. The tabu search (TS) algorithm is employed to search the SVR parameters. Moreover, various tabu list sizes and neighbor solutions are used to examine the forecasting performance of SVRTS models. The experimental results revel that the SARTS model provides better forecasting results than the seasonal autoregressive integrated moving average (SARIMA) model. Thus, the developed SVRTS model is an appropriate alternative for forecasting highway traffic.
Starting Page	1617
Ending Page	1621
File Size	251476
Page Count	5
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Road transportation Traffic control Predictive models Telecommunication traffic Communication system traffic control Electronic mail Artificial neural networks Technology management Engineering management Neural networks
Content Type	Text
Resource Type	Article

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