  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 space among traffic
flow and traffic density. One of the LSTM NNs, called a discriminative network,
aims to maximize the probability of assigning correct labels to both true
traffic state matrices (i.e., traffic flow and traffic density within a given
spatial-temporal area) and the traffic state matrices generated from the other
neural network. The other LSTM NN, called a generative network, aims to
generate traffic state matrices which maximize the probability that the
discriminative network assigns true labels to them. The two LSTM NNs are
trained simultaneously such that the trained generative network can generate
traffic matrices similar to those in the training data set. Given a traffic
state matrix with missing values, we use back-propagation on three defined loss
functions to map the corrupted matrix to a latent space. The mapping vector is
then passed through the pre-trained generative network to estimate the missing
values of the corrupted matrix. The proposed GAA is compared with the existing
Bayesian network approach on loop detector data collected from Seattle,
Washington and that collected from San Diego, California. Experimental results
indicate that the GAA can achieve higher accuracy in traffic state estimation
than the Bayesian network approach.
