  Origin-destination (OD) flow, which contains valuable population mobility
information including direction and volume, is critical in many urban
applications, such as urban planning, transportation management, etc. However,
OD data is not always easy to access due to high costs or privacy concerns.
Therefore, we must consider generating OD through mathematical models. Existing
works utilize physics laws or machine learning (ML) models to build the
association between urban structures and OD flows while these two kinds of
methods suffer from the limitation of over-simplicity and poor generalization
ability, respectively. In this paper, we propose to adopt physics-informed ML
paradigm, which couple the physics scientific knowledge and data-driven ML
methods, to construct a model named Origin-Destination Generation Networks
(ODGN) for better population mobility modeling by leveraging the complementary
strengths of combining physics and ML methods. Specifically, we first build a
Multi-view Graph Attention Networks (MGAT) to capture the urban features of
every region and then use a gravity-guided predictor to obtain OD flow between
every two regions. Furthermore, we use a conditional GAN training strategy and
design a sequence-based discriminator to consider the overall topological
features of OD as a network. Extensive experiments on real-world datasets have
been done to demonstrate the superiority of our proposed method compared with
baselines.

Machine Learning