# Origin-Destination Network Generation via Gravity-Guided GAN

arXiv: https://arxiv.org/abs/2306.03390 · HTML: https://mlsanity.com/paper/2306.03390/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles](https://mlsanity.com/paper/2001.11086/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles](https://mlsanity.com/paper/1810.13075/) (2018)
- [Gravity-Inspired Graph Autoencoders for Directed Link Prediction](https://mlsanity.com/paper/1905.09570/) (2019)
- [Learning Geo-Contextual Embeddings for Commuting Flow Prediction](https://mlsanity.com/paper/2005.01690/) (2020)
- [Latent Space Model for Road Networks to Predict Time-Varying Traffic](https://mlsanity.com/paper/1602.04301/) (2016)
- [A Machine Learning Approach to Modeling Human Migration](https://mlsanity.com/paper/1711.05462/) (2017)
- [Reconstructing commuters network using machine learning and urban indicators](https://mlsanity.com/paper/1908.03512/) (2019)
- [Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows](https://mlsanity.com/paper/1911.11380/) (2019)

## Built on by (most cited first, 8 of 8)

- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction](https://mlsanity.com/paper/2506.13678/) (2025)
- [OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction](https://mlsanity.com/paper/2607.03200/) (2026)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [AMBIT: Augmenting Mobility Baselines with Interpretable Trees](https://mlsanity.com/paper/2512.22466/) (2025)
- [Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction](https://mlsanity.com/paper/2603.19599/) (2026)
- [Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models](https://mlsanity.com/paper/2608.21567/) (2026)
- [Inferring Urban Mobility Interactions from Aggregated Dynamics](https://mlsanity.com/paper/2609.07349/) (2026)

## Abstract (arXiv)

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.