  Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to a lack of mining road
topology. To address the effect attenuation problem, we suggest taking into
account the traffic of surrounding locations(wider than the adjacent range). We
propose an end-to-end framework called DeepTransport, in which Convolutional
Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to
obtain spatial-temporal traffic information within a transport network
topology. In addition, an attention mechanism is introduced to align spatial
and temporal information. Moreover, we constructed and released a real-world
large traffic condition dataset with a 5-minute resolution. Our experiments on
this dataset demonstrate our method captures the complex relationship in the
temporal and spatial domains. It significantly outperforms traditional
statistical methods and a state-of-the-art deep learning method.
