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: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: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.
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.
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...
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.
MotiveMob is a motivation-driven autoregressive framework for generating human mobility trajectories. It models the "why-where-when" decision process by first predicting a plausible motivation for each movement step and then jointly generating the next location and arrival time. The approach is evaluated under user-level and temporal distribution shifts, including seasonal changes and COVID-19 disruptions, and consistently outperforms pretraining- and prompting-based baselines in distributional fidelity.
arXiv:2610.17672v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility prefer...
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic 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:2607. 03394v1 Announce Type: new Abstract: Real time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation.
arXiv:2608. 14570v1 Announce Type: new Abstract: Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning.