arXiv:2606. 01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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. 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.
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:2602. 16727v2 Announce Type: replace Abstract: Simulating large-scale human mobility is fundamental to understanding population movement patterns and supporting real-world geospatial applications such as urban planning, epidemic response, and transportation analysis.
CityReal is a modular framework that uses large language model agents to simulate human-aligned urban behavior. It models agents as intention-driven decision makers who pursue coherent mobility and activity plans, learning habits and preferences over time. By training textual adapters to align agent decisions with observed population statistics, CityReal improves realism at both micro and macro levels and can scale to tens of thousands of agents for analyzing crowd density, place popularity, mobility flows, and well‑being under various urban scenarios.
arXiv:2606. 03678v1 Announce Type: new Abstract: Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures while preserving realism.