Hong Zhang1,2,*, Gang Yang1, Hailiang Yu1, Zan Zheng1
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274
Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results… More >