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- W2387858462 abstract "Intersections are the key nodes of road transportation with obvious nonlinear characteristics of the magnitude of traffic flow.In this paper,a two-stage forecasting model is established for forecasting intersection traffic flow features.By simulating all intersections with traffic simulation technology,traffic flow of each direction of intersections is acquired at first.Then,intersection traffic volumes of all directions are forecasted with neural network training.A forecasting model with high accuracy and good generalization performance is proposed with constant iteration.Finally,survey data of 31 typical intersections in Guiyang is chosen as sample data.Traffic flow of each direction of intersections and observed value of 18 intersections are served as input / output factors to the neural network model,the neural network model is constructed by training whose effectiveness has been verified.Therefore,the remaining intersections traffic flow is served as input factor,and amendatory traffic flow of intersection approach lanes as the output factor.The results indicate that this approach has advantages of fast convergence rate and small amount of calculation which can be well applied to forecasting traffic flow of intersection." @default.
- W2387858462 created "2016-06-24" @default.
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- W2387858462 date "2013-01-01" @default.
- W2387858462 modified "2023-09-23" @default.
- W2387858462 title "Study on Prediction of Intersection Traffic Flow Simulation Based on Neural Network Model" @default.
- W2387858462 hasPublicationYear "2013" @default.
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