Origin-Destination Network Generation via Gravity-Guided GAN

TL;DR

This paper introduces ODGN, a physics-informed machine learning model combining graph attention networks and GANs to generate realistic origin-destination flows for urban mobility analysis.

Contribution

The paper proposes a novel physics-guided ML framework that integrates graph attention networks and GANs for more accurate OD flow generation.

Findings

Outperforms baseline models on real-world datasets

Effectively captures urban features and OD topological structures

Demonstrates improved generalization in OD flow prediction

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.