  The Origin-Destination~(OD) networks provide an estimation of the flow of
people from every region to others in the city, which is an important research
topic in transportation, urban simulation, etc. Given structural regional urban
features, generating the OD network has become increasingly appealing to many
researchers from diverse domains. However, existing works are limited in
independent generation of each OD pair, i.e., flow of people from one region to
another, overlooking the relations within the overall network. In this paper,
we instead propose to generate the OD network, and design a graph denoising
diffusion method to learn the conditional joint probability distribution of the
nodes and edges within the OD network given city characteristics at region
level. To overcome the learning difficulty of the OD networks covering over
thousands of regions, we decompose the original one-shot generative modeling of
the diffusion model into two cascaded stages, corresponding to the generation
of network topology and the weights of edges, respectively. To further
reproduce important network properties contained in the city-wide OD network,
we design an elaborated graph denoising network structure including a node
property augmentation module and a graph transformer backbone. Empirical
experiments on data collected in three large US cities have verified that our
method can generate OD matrices for new cities with network statistics
remarkably similar with the ground truth, further achieving superior
outperformance over competitive baselines in terms of the generation realism.
