Title :
Short-Term Prediction of Intelligent Traffic Flow Based on BP Neural Network and ARIMA Model
Author :
Guo, Xin ; Deng, Feiqi
Author_Institution :
Autom. Sci. & Eng. Coll., South China Univ. of Technol., Guangzhou, China
Abstract :
Short-term prediction of intelligent traffic flow is in favor of road unblocked and vehicle waiting strategy. The algorithm of short-term prediction of intelligent traffic flow based on back propagation(BP) neural network and autoregressive integrated moving average (ARIMA) model can solve it partially. Firstly, Establishing a BP neural network sub-model and ARIMA sub-model, Then taking BP neural network as the approaching machine of the most superior nonlinear combination model to establish the hybrid prediction model, and the results indicate that the hybrid prediction method is practical and feasible.
Keywords :
autoregressive moving average processes; backpropagation; neural nets; road traffic; transportation; ARIMA model; BP neural network; autoregressive integrated moving average model; back propagation; hybrid prediction model; intelligent traffic flow; nonlinear combination model; road unblocked strategy; short-term prediction; vehicle waiting strategy; Artificial neural networks; Forecasting; Mathematical model; Predictive models; Roads; Training;
Conference_Titel :
E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on
Conference_Location :
Henan
Print_ISBN :
978-1-4244-7159-1
DOI :
10.1109/ICEEE.2010.5660398