Chapter
Jun 29, 2016
Forecasting the Intersection Traffic Volume Based on the Levenberg-Marquardt Algorithm
Authors: Fang Wu [email protected], Junfeng Zhang [email protected], and Changxi Ma [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
Real time and accurate predictions of intersections help set scientific traffic signal programs, expand road capacity and improve traffic conditions. The paper establishes a short-term forecasting model of intersection channel imports according to the Levenberg-Marquardt (LM) neural network algorithm, which is based on the analysis of intersection traffic volume time and spatial correlation, combining LM neural network distributed processing, self-organizing, adaptive, self-learning, and other good characteristics. Using MATLAB to forecast short-term traffic volume of intersection imports with the prediction model and some specific examples, the empirical results show that the prediction model has preferable prediction accuracy.
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Published In
CICTP 2016
Pages: 337 - 345
Copyright
© 2016 American Society of Civil Engineers.
History
Published online: Jun 29, 2016
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Affiliations
School of Traffic and Transportation, Lanzhou Jiaotong Univ., P.O. Box 730070, Lanzhou City, Gansu Province. E-mail: [email protected]
School of Traffic and Transportation, Lanzhou Jiaotong Univ., P.O. Box 730070, Lanzhou City, Gansu Province. E-mail: [email protected]
School of Traffic and Transportation, Lanzhou Jiaotong Univ., P.O. Box 730070, Lanzhou City, Gansu Province. E-mail: [email protected]
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