The paper presents a method to align a language‑model‑based crowd agent with aggregate mobility data by fine‑tuning it to match observed destination compositions derived from origin‑to‑destination flows. The approach uses iterative proportional fitting to reweight the model’s destination distribution and corrects for dominant destination inflation by training a low‑rank adapter on resampled trajectories. Experiments on mobile network counts from two baseball games show a 25% reduction in destination‑share error while maintaining similar grid correlation across policies.
arXiv:2608. 19778v1 Announce Type: cross Abstract: Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual trajectories.
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.
The paper demonstrates that real‑time urban mobility patterns can be reconstructed from aggregated area‑level counts, without tracking individuals. Using a physics‑informed, uncertainty‑aware framework, the authors infer future origin‑destination flows across twelve datasets from the U.S. and China, achieving accuracy comparable to models that use historical OD matrices. Probabilistic modeling corrects underestimation of sparse corridors, and architectures that preserve spatial heterogeneity before reconstructing pairwise interactions yield more faithful interaction estimates.
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. 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.
The paper demonstrates that fine‑tuning large language models (LLMs) on local tourist trajectory data can predict visitor movements under varying conditions. Using 566 trajectories from Wakayama Castle Park, Japan, the authors fine‑tuned Llama‑3.1‑8B, achieving 49.1% accuracy for next point‑of‑interest predictions and maintaining strong performance even on undersampled scenarios such as rainy days. This shows that LLMs can serve as high‑fidelity, context‑aware behavior models for tourist prediction and enable counterfactual analysis of mobility interventions.
arXiv:2606. 01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features.
arXiv:2606. 31209v1 Announce Type: new Abstract: Interactive traffic simulation is a vital world model for autonomous driving.
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.