  Traffic demand forecasting by deep neural networks has attracted widespread
interest in both academia and industry society. Among them, the pairwise
Origin-Destination (OD) demand prediction is a valuable but challenging problem
due to several factors: (i) the large number of possible OD pairs, (ii)
implicitness of spatial dependence, and (iii) complexity of traffic states. To
address the above issues, this paper proposes a Continuous-time and Multi-level
dynamic graph representation learning method for Origin-Destination demand
prediction (CMOD). Firstly, a continuous-time dynamic graph representation
learning framework is constructed, which maintains a dynamic state vector for
each traffic node (metro stations or taxi zones). The state vectors keep
historical transaction information and are continuously updated according to
the most recently happened transactions. Secondly, a multi-level structure
learning module is proposed to model the spatial dependency of station-level
nodes. It can not only exploit relations between nodes adaptively from data,
but also share messages and representations via cluster-level and area-level
virtual nodes. Lastly, a cross-level fusion module is designed to integrate
multi-level memories and generate comprehensive node representations for the
final prediction. Extensive experiments are conducted on two real-world
datasets from Beijing Subway and New York Taxi, and the results demonstrate the
superiority of our model against the state-of-the-art approaches.
