Traffic speed prediction of regional complex road networks integrating CapsNet with D-BiLSTM
Due to the complex and dynamic spatio-temporal correlation of traffic patterns leads to the inadequacy of existing methods to learn traffic evolution in terms of structural depth and prediction scale.a Deep learning model combining CapsNet and deep bi-directional LSTM(D-BiLSTM)was proposed.This model was used to identify the spatial topology of road networks and extract spatial features using CapsNet,was fused with the D-BiLSTM network,taking into account both the forward and backward dependencies of traffic states,and capturing the bi-directional temporal correlations of different historical periods,to forecast traffic on large-scale complex road networks in the target region.Experiments conducted on real traffic road network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10%on average,outperforming other methods,with high prediction accuracy and good robustness in traffic prediction of regional complex road networks.
万方数据
维普
