arXiv:2610.31944v1 Announce Type: new Abstract: Emergency medical dispatch increasingly relies on AI-enabled decision support systems when making ambulance dispatch decisions. In this process, accura...
arXiv:2606. 00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation.
arXiv:2608. 00815v1 Announce Type: new Abstract: The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify.
arXiv:2606. 01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.
OPTIC-ER is a reinforcement learning framework designed to improve emergency response in underserved African communities by providing real‑time, adaptive, and equitable dispatch decisions. It employs an attention‑guided actor‑critic architecture with a Context‑Rich State Vector and a Precision Reward Function, trained on high‑fidelity simulations using real data from Rivers State, Nigeria. In tests on 500 unseen incidents, OPTIC-ER reduced response times by over 35% and surpassed the nearest‑facility heuristic’s 62.94% optimal‑selection rate, while also producing infrastructure deficiency maps and equity monitoring dashboards.
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.
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. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.
HealthMamba is an uncertainty‑aware spatiotemporal graph state space model designed to predict healthcare facility visits more accurately and reliably. It integrates a Unified Spatiotemporal Context Encoder, a novel GraphMamba module for hierarchical modeling, and a comprehensive uncertainty quantification component. Evaluated on large‑scale datasets from California, New York, Texas, and Florida, it improves prediction accuracy by about 6.0% and uncertainty quantification by 3.5% over state‑of‑the‑art baselines.
arXiv:2606. 07622v1 Announce Type: new Abstract: Accurate passenger queue forecasting in airport terminals is essential for efficient departure operations, as it enables proactive congestion management.
This study analyzes 2,850,575 ambulance attendances in Victoria, Australia, to quantify ambulance ramping—the delay between hospital arrival and patient handover—across 59 hospitals and 79 local government areas from January 2020 to March 2024. It finds that cumulative ambulance hours lost (AHL) totalled 1,491,127 hours, with ten hospitals accounting for 57.8 % of lost hours. Ramping duration increases with the number of ambulances arriving at the same hospital in the preceding hour and shows a moderate correlation with hourly demand two to four hours later.
arXiv:2607. 01082v1 Announce Type: new Abstract: Spatio-temporal point-process models must often generalise across space when local event histories are sparse.