arXiv AI
Beyond Trajectory Statistics: Community-Consistent Human Mobility Generation via Evolving User-Location Graphs
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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.
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
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Deep Generative Model for Human Mobility Behavior
arXiv:2510. 06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health.
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When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
arXiv:2606. 13835v1 Announce Type: cross Abstract: LLM-based generative agents are increasingly used in urban simulators, yet it remains unclear whether they reproduce empirically realistic human mobility patterns or merely generate plausible mobility narratives.
Hugging Face Trending Papers
4d ago
ScaleTraj: Scale-Decomposed Human Trajectory Generation
Individual-level trajectories are essential for applications that rely on user-level mobility behavior. Existing mobility-generation approaches face a fundamental tension between individual-level and...
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.