Origin-Destination Network Generation via Gravity-Guided GAN

• URL: http://arxiv.org/abs/2306.03390v1
• Date: Tue, 6 Jun 2023 04:07:21 GMT
• Title: Origin-Destination Network Generation via Gravity-Guided GAN
• Authors: Can Rong, Huandong Wang, Yong Li
• Abstract summary: Origin-destination (OD) flow contains valuable population mobility information including direction and volume. We propose to construct a model named Origin-Destination Generation Networks (ODGN) for better population mobility modeling. 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.
• Score: 9.03056486066899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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