  Space-time visualizations of macroscopic or microscopic traffic variables is
a qualitative tool used by traffic engineers to understand and analyze
different aspects of road traffic dynamics. We present a deep learning method
to learn the macroscopic traffic speed dynamics from these space-time
visualizations, and demonstrate its application in the framework of traffic
state estimation. Compared to existing estimation approaches, our approach
allows a finer estimation resolution, eliminates the dependence on the initial
conditions, and is agnostic to external factors such as traffic demand, road
inhomogeneities and driving behaviors. Our model respects causality in traffic
dynamics, which improves the robustness of estimation. We present the
high-resolution traffic speed fields estimated for several freeway sections
using the data obtained from the Next Generation Simulation Program (NGSIM) and
German Highway (HighD) datasets. We further demonstrate the quality and utility
of the estimation by inferring vehicle trajectories from the estimated speed
fields, and discuss the benefits of deep neural network models in approximating
the traffic dynamics.
