Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
arXiv:2603.05581v2 Announce Type: replace-cross Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...
The study investigates whether large language models (LLMs) can predict neighborhood-level human mobility without training data. Using anonymized Cuebiq data across four U.S. metropolitan areas, the authors compare zero‑shot LLM predictions to supervised baselines for various mobility outcomes and assess structural alignment with empirical trends. Results show supervised models outperform LLMs (average accuracy 0.580 vs. 0.435), with LLMs relying on coarse, stable priors that may exhibit biased treatment of protected-group predictors.
PlacePulse is a structural geospatial foundation model that transforms 572 attributes—expanded to 2,288 through H3 neighborhood aggregates—into 256‑dimensional unit‑norm embeddings for over a million H3 resolution‑7 cells across the United States. The model achieves high intrinsic reconstruction quality (mean $R^2 = 0.853$) and strong spatial organization (Moran's $I = 0.93$), while outperforming a dimensionality‑matched baseline on 74 held‑out targets from 12 external sources and competing with Google’s Population Dynamics Foundation Model on various health, economic, election, mobility, and social‑vulnerability outcomes. These results demonstrate that static structural composition can serve as a distinct and complementary modality for geospatial foundation models.
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
LE4Mob is a new location embedding framework that learns inductive, distance‑aware representations from geographic context, enabling it to encode unseen locations and preserve spatial relationships. It builds on contrastive language‑location pre‑training and adds a regularisation objective that encourages the embedding space to reflect geographic distance. Experiments on next‑location prediction and commuter flow generation across multiple datasets show that LE4Mob outperforms strong baselines, especially in inductive settings and when downstream models use direct interactions between location embeddings.