Deep Sequence Learning with Auxiliary Information for Traffic Prediction

Understand

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.