A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation
A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation is a scholarly work, published in 2018 in ''Transportation Research Record''. The main subjects of the publication include Traffic generation model, traffic collision, data mining, traffic flow, artificial intelligence, generative grammar, generative model, discriminative model, artificial neural network, traffic flow, machine learning, pattern recognition, computer science, and Bayesian probability. This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic-state estimation.