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.
Forecasting the Intersection Traffic Volume Based on the Levenberg-Marquardt Algorithm
16th COTA International Conference of Transportation Professionals ; 2016 ; Shanghai, China
CICTP 2016 ; 337-345
2016-07-01
Conference paper
Electronic Resource
English
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