# Learning Geo-Contextual Embeddings for Commuting Flow Prediction

arXiv: https://arxiv.org/abs/2005.01690 · HTML: https://mlsanity.com/paper/2005.01690/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 28 of 28)

- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion](https://mlsanity.com/paper/2309.10547/) (2023)
- [Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model](https://mlsanity.com/paper/2306.04873/) (2023)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Pretrained Mobility Transformer: A Foundation Model for Human Mobility](https://mlsanity.com/paper/2406.02578/) (2024)
- [Harnessing LLMs for Cross-City OD Flow Prediction](https://mlsanity.com/paper/2409.03937/) (2024)
- [Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)](https://mlsanity.com/paper/2202.09021/) (2022)
- [Jointly spatial-temporal representation learning for individual trajectories](https://mlsanity.com/paper/2312.04055/) (2023)
- [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)
- [Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2402.06135/) (2024)
- [Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction](https://mlsanity.com/paper/2408.14762/) (2024)
- [Generating Origin-Destination Matrices in Neural Spatial Interaction Models](https://mlsanity.com/paper/2410.07352/) (2024)
- [Transportation Scenario Planning with Graph Neural Networks](https://mlsanity.com/paper/2110.13202/) (2021)
- [RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks](https://mlsanity.com/paper/2501.17599/) (2025)
- [Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities](https://mlsanity.com/paper/2503.06398/) (2025)
- [Sat2Flow: A Structure-Aware Diffusion Framework for Human Flow Generation from Satellite Imagery](https://mlsanity.com/paper/2508.19499/) (2025)
- [VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns](https://mlsanity.com/paper/2510.02702/) (2025)
- [Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation](https://mlsanity.com/paper/2605.00938/) (2026)
- [DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling](https://mlsanity.com/paper/2606.09086/) (2026)
- [SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2607.05259/) (2026)
- [EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation](https://mlsanity.com/paper/2607.22655/) (2026)
- [Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models](https://mlsanity.com/paper/2608.21567/) (2026)
- [Do Satellites See Commuters? A Critical Benchmark of Vision Foundation Models](https://mlsanity.com/paper/2609.00661/) (2026)
- [Inferring Urban Mobility Interactions from Aggregated Dynamics](https://mlsanity.com/paper/2609.07349/) (2026)
- [LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling](https://mlsanity.com/paper/2609.22117/) (2026)
- [SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark](https://mlsanity.com/paper/2610.13730/) (2026)

## Abstract (arXiv)

Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting flows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the influence of nearby regions. To address these issues, we propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction. Specifically, we first construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. A multitask learning framework is used to introduce stronger restrictions and enhance the effectiveness of the embedding representation. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting flows. We evaluate our model using real-world datasets from New York City and the experimental results demonstrate the effectiveness of our proposal against the state of the art.