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Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

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Academic year: 2021

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Figure

Figure 1. (a) A snapshot with traffic congestion levels from WSDOT. (b) Only the road network is retained after preprocessing
Figure 2. The architecture of our proposed deep neural network for traffic congestion prediction.
Figure 3. A sample of a sequence of images with only road networks organized chronologically for further transformations.
Table 1. Comparison of prediction metrics by different configurations of DCPN. Minimum wMSE values marked in bold.
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