  Deep neural networks have recently demonstrated the traffic prediction
capability with the time series data obtained by sensors mounted on road
segments. However, capturing spatio-temporal features of the traffic data often
requires a significant number of parameters to train, increasing computational
burden. In this work we demonstrate that embedding topological information of
the road network improves the process of learning traffic features. We use a
graph of a vehicular road network with recurrent neural networks (RNNs) to
infer the interaction between adjacent road segments as well as the temporal
dynamics. The topology of the road network is converted into a spatio-temporal
graph to form a structural RNN (SRNN). The proposed approach is validated over
traffic speed data from the road network of the city of Santander in Spain. The
experiment shows that the graph-based method outperforms the state-of-the-art
methods based on spatio-temporal images, requiring much fewer parameters to
train.
