  This paper presents a dedicated Deep Neural Network (DNN) architecture that
reconstructs space-time traffic speeds on freeways given sparse data. The DNN
is constructed in such a way, that it learns heterogeneous congestion patterns
using a large dataset of sparse speed data, in particular from probe vehicles.
Input to the DNN are two equally sized input matrices: one containing raw
measurement data, and the other indicates the cells occupied with data. The
DNN, comprising multiple stacked convolutional layers with an encoding-decoding
structure and feed-forward paths, transforms the input into a full matrix of
traffic speeds. The proposed DNN architecture is evaluated with respect to its
ability to accurately reconstruct heterogeneous congestion patterns under
varying input data sparsity. Therefore, a large set of empirical Floating-Car
Data (FCD) collected on German freeway A9 during two months is utilized. In
total, 43 congestion distinct scenarios are observed which comprise moving and
stationary congestion patterns. A data augmentation technique is applied to
generate input-output samples of the data, which makes the DNN shift-invariant
as well as capable of managing varying data sparsities. The DNN is trained and
subsequently applied to sparse data of an unseen congestion scenario. The
results show that the DNN is able to apply learned patterns, and reconstructs
moving as well as stationary congested traffic with high accuracy; even given
highly sparse input data. Reconstructed speeds are compared qualitatively and
quantitatively with the results of several state-of-the-art methods such as the
Adaptive Smoothing Method (ASM), the Phase-Based Smoothing Method (PSM) and a
standard Convolutional Neural Network (CNN) architecture. As a result, the DNN
outperforms the other methods significantly.
