  Short-term road traffic prediction (STTP) is one of the most important
modules in Intelligent Transportation Systems (ITS). However, network-level
STTP still remains challenging due to the difficulties both in modeling the
diverse traffic patterns and tacking high-dimensional time series with low
latency. Therefore, a framework combining with a deep clustering (DeepCluster)
module is developed for STTP at largescale networks in this paper. The
DeepCluster module is proposed to supervise the representation learning in a
visualized way from the large unlabeled dataset. More specifically, to fully
exploit the traffic periodicity, the raw series is first split into a number of
sub-series for triplets generation. The convolutional neural networks (CNNs)
with triplet loss are utilized to extract the features of shape by transferring
the series into visual images. The shape-based representations are then used
for road segments clustering. Thereafter, motivated by the fact that the road
segments in a group have similar patterns, a model sharing strategy is further
proposed to build recurrent NNs (RNNs)-based predictions through a group-based
model (GM), instead of individual-based model (IM) in which one model are built
for one road exclusively. Our framework can not only significantly reduce the
number of models and cost, but also increase the number of training data and
the diversity of samples. In the end, we evaluate the proposed framework over
the network of Liuli Bridge in Beijing. Experimental results show that the
DeepCluster can effectively cluster the road segments and GM can achieve
comparable performance against the IM with less number of models.
