arXiv:2607. 19312v1 Announce Type: new Abstract: Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications.
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 Machine Learning.
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:2505.11239v4 Announce Type: replace Abstract: Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning,...
arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.
The paper introduces a cluster-based method for predicting dense crowd trajectories, grouping individuals with similar attributes over time to accelerate processing. By replacing individual pedestrian inputs with cluster centroids, the approach reduces computational cost and memory usage while preserving accuracy. Extensive evaluations on challenging dense crowd scenes confirm these benefits compared to state‑of‑the‑art methods.
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.
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.