A Knowledge-Grounded Behavioral Reasoning Framework for Training-Free Urban Healthcare OD Prediction

Abstract

Understanding urban healthcare mobility is essential for healthcare resource allocation, hospital capacity planning, and resilient urban governance. Existing healthcare origin-destination (OD) prediction methods primarily rely on supervised deep learning, requiring expensive model training while providing limited interpretability into the behavioral mechanisms of hospital choice. This paper presents a training-free urban healthcare OD prediction framework based on knowledge-grounded behavioral reasoning, replacing gradient-based neural network learning with collaborative LLM reasoning over structured urban knowledge. The framework organizes heterogeneous urban information, including healthcare resources, population characteristics, transportation accessibility, weather conditions, and historical mobility, into a unified knowledge graph, enabling evidence retrieval, urban context understanding, behavioral reasoning, preference ranking, and Bayesian verification through a multi-agent reasoning pipeline. Experiments on a real-world Shenzhen dataset containing 137 hospitals and 6,341 prediction tasks demonstrate that the proposed framework achieves CPC = 0.5449, Top-1 = 66.19%, Top-5 = 68.03%, and Top-10 = 74.03%, consistently outperforming conventional deep learning baselines on all Top-K metrics (e.g., Top-5 improves by 22.1% over MLP). Compared with Direct LLM using the same backbone without structured reasoning, the proposed framework improves CPC by 87.5% and Top-1 accuracy by 141%, demonstrating that knowledge-grounded behavioral reasoning, rather than raw LLM capability, is the key to accurate, interpretable, and training-free healthcare OD prediction. These findings highlight the potential of urban intelligence to move healthcare mobility modeling beyond data fitting toward knowledge-driven behavioral reasoning.