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A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation
This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic-state estimation. The GAA is able to combine traffic-flow theory with neural networks and thus improve the accuracy of traffic-state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and …