  Predicting traffic conditions from online route queries is a challenging task
as there are many complicated interactions over the roads and crowds involved.
In this paper, we intend to improve traffic prediction by appropriate
integration of three kinds of implicit but essential factors encoded in
auxiliary information. We do this within an encoder-decoder sequence learning
framework that integrates the following data: 1) offline geographical and
social attributes. For example, the geographical structure of roads or public
social events such as national celebrations; 2) road intersection information.
In general, traffic congestion occurs at major junctions; 3) online crowd
queries. For example, when many online queries issued for the same destination
due to a public performance, the traffic around the destination will
potentially become heavier at this location after a while. Qualitative and
quantitative experiments on a real-world dataset from Baidu have demonstrated
the effectiveness of our framework.
