  This paper proposes a deep learning approach for traffic flow prediction in
complex road networks. Traffic flow data from induction loop sensors are
essentially a time series, which is also spatially related to traffic in
different road segments. The spatio-temporal traffic data can be converted into
an image where the traffic data are expressed in a 3D space with respect to
space and time axes. Although convolutional neural networks (CNNs) have been
showing surprising performance in understanding images, they have a major
drawback. In the max pooling operation, CNNs are losing important information
by locally taking the highest activation values. The inter-relationship in
traffic data measured by sparsely located sensors in different time intervals
should not be neglected in order to obtain accurate predictions. Thus, we
propose a neural network with capsules that replaces max pooling by dynamic
routing. This is the first approach that employs the capsule network on a time
series forecasting problem, to our best knowledge. Moreover, an experiment on
real traffic speed data measured in the Santander city of Spain demonstrates
the proposed method outperforms the state-of-the-art method based on a CNN by
13.1% in terms of root mean squared error.
