Hugging Face Trending Papers
ScaleTraj: Scale-Decomposed Human Trajectory Generation
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arXiv Machine Learning
Oct 3
Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation
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 AI
Jun 2
CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation
arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.
arXiv AI
Jun 10
Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
arXiv:2606. 10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets.
arXiv AI
Oct 21
Beyond Trajectory Statistics: Community-Consistent Human Mobility Generation via Evolving User-Location Graphs
arXiv:2610.28467v1 Announce Type: new Abstract: Human mobility data are fundamental to Web-based location services and broader urban applications, and mobility generation provides synthetic trajector...
arXiv AI
Aug 25
Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models
arXiv:2608.21567v1 Announce Type: new Abstract: Human mobility serves as an essential proxy for understanding social, economic, and environmental dynamics in urban systems. Geospatial transferability...
arXiv AI
Jul 10
MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
arXiv:2607. 08357v1 Announce Type: new Abstract: Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns.