# Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

arXiv: https://arxiv.org/abs/2012.00489 · HTML: https://mlsanity.com/paper/2012.00489/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 30 of 58)

- [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023)
- [ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model](https://mlsanity.com/paper/2404.15380/) (2024)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024)
- [Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion](https://mlsanity.com/paper/2309.10547/) (2023)
- [Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning](https://mlsanity.com/paper/2502.06870/) (2025)
- [Human mobility is well described by closed-form gravity-like models learned automatically from data](https://mlsanity.com/paper/2312.11281/) (2023)
- [A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards](https://mlsanity.com/paper/2206.10598/) (2022)
- [Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model](https://mlsanity.com/paper/2306.04873/) (2023)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors](https://mlsanity.com/paper/2412.05000/) (2024)
- [Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks](https://mlsanity.com/paper/2211.08903/) (2022)
- [CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches](https://mlsanity.com/paper/2309.11587/) (2023)
- [MobilityDL: A Review of Deep Learning From Trajectory Data](https://mlsanity.com/paper/2402.00732/) (2024)
- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network](https://mlsanity.com/paper/2206.05731/) (2022)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models](https://mlsanity.com/paper/2603.15009/) (2026)
- [TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction](https://mlsanity.com/paper/2402.15398/) (2024)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction](https://mlsanity.com/paper/2506.13678/) (2025)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [Transferable Human Mobility Network Reconstruction with neuroGravity](https://mlsanity.com/paper/2604.23678/) (2026)
- [Enhancing crowd flow prediction in various spatial and temporal granularities](https://mlsanity.com/paper/2203.07372/) (2022)
- [FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation](https://mlsanity.com/paper/2307.11214/) (2023)
- [Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction](https://mlsanity.com/paper/2408.14762/) (2024)
- [Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation](https://mlsanity.com/paper/2602.17972/) (2026)

## Abstract (arXiv)

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose the Deep Gravity model, an effective method to generate flow probabilities that exploits many variables (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those variables and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity has good geographic generalization capability, achieving a significant increase in performance (especially in densely populated regions of interest) with respect to the classic gravity model and models that do not use deep neural networks or geographic data. We also show how flows generated by Deep Gravity may be explained in terms of the geographic features using explainable AI techniques.