  We propose a statistical learning-based traffic speed estimation method that
uses sparse vehicle trajectory information. Using a convolutional
encoder-decoder based architecture, we show that a well trained neural network
can learn spatio-temporal traffic speed dynamics from time-space diagrams. We
demonstrate this for a homogeneous road section using simulated vehicle
trajectories and then validate it using real-world data from NGSIM. Our results
show that with probe vehicle penetration levels as low as 5\%, the proposed
estimation method can provide a sound reconstruction of macroscopic traffic
speeds and reproduce realistic shockwave patterns, implying applicability in a
variety of traffic conditions. We further discuss the model's reconstruction
mechanisms and confirm its ability to differentiate various traffic behaviors
such as congested and free-flow traffic states, transition dynamics, and
shockwave propagation.
