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1. A Novel STFSA
for Short-Term Traffic Speed Prediction
Abstract
Short-term traffic speed prediction is fundamental to intelligent transportation
systems (ITS), and the accuracy of the model largely determines the
performance of real-time traffic control and management. In this study, a
short-term traffic speed predicti
analysis of traffic flow and a combined deep
spatial-temporal feature selection algorithm (STFSA) of a convolutional neural
network–gated recurrent unit (CNN
the STFSA is firstly employed to reconstruct the spatial
traffic speed based on temporal continuity and spatial characteristics, and
then this matrix is considered as the input feature of the prediction model.
After this, the nonlinear fitting ability of the CNN is adopted to extract deep
features from the convolutional and pooling layers for model training. Finally,
by combining the timing and long
the forward GRU and the reverse
A Novel STFSA-CNN-GRU Hybrid Model
Term Traffic Speed Prediction
term traffic speed prediction is fundamental to intelligent transportation
systems (ITS), and the accuracy of the model largely determines the
time traffic control and management. In this study, a
term traffic speed prediction method based on the spatial
analysis of traffic flow and a combined deep-learning model, and a hybrid
temporal feature selection algorithm (STFSA) of a convolutional neural
gated recurrent unit (CNN-GRU)) is initially developed.
the STFSA is firstly employed to reconstruct the spatial-temporal matrix of
traffic speed based on temporal continuity and spatial characteristics, and
then this matrix is considered as the input feature of the prediction model.
the nonlinear fitting ability of the CNN is adopted to extract deep
features from the convolutional and pooling layers for model training. Finally,
by combining the timing and long-range dependence of the captured data with
the forward GRU and the reverse GRU, the accuracy of the prediction result is
GRU Hybrid Model
Term Traffic Speed Prediction
term traffic speed prediction is fundamental to intelligent transportation
systems (ITS), and the accuracy of the model largely determines the
time traffic control and management. In this study, a
on method based on the spatial-temporal
learning model, and a hybrid
temporal feature selection algorithm (STFSA) of a convolutional neural
GRU)) is initially developed. Specifically,
temporal matrix of
traffic speed based on temporal continuity and spatial characteristics, and
then this matrix is considered as the input feature of the prediction model.
the nonlinear fitting ability of the CNN is adopted to extract deep
features from the convolutional and pooling layers for model training. Finally,
range dependence of the captured data with
GRU, the accuracy of the prediction result is

2. further improved. The validity of the proposed model can be verified by
comparing the prediction results with the actual traffic data. Accordingly, in the
case study, the performance is compared with various benchmark methods
under the same prediction scenario, verifying the superiority of the proposed
model.