Efficient emergency vehicle routing in urban environments is critical for timely medical response, yet existing approaches often decouple traffic prediction from routing, overlook heterogeneous uncertainty, and lack causal reasoning under routing interventions. We propose an end-to-end differentiable framework that integrates risk-aware routing, causal traffic forecasting, and decomposed uncertainty quantification. Specifically, a regime-conditioned evidential heterogeneous spatiotemporal graph neural network models traffic dynamics on heterogeneous road networks while separately estimating aleatoric and epistemic uncertainty across road segments. To capture intervention effects, a causal graph neural network learns dynamic causal dependencies and enables counterfactual prediction of traffic changes induced by emergency routing decisions. Building on these components, we design a multi-objective routing strategy that adaptively balances travel time, reliability, and safety risk according to real-time hospital capacity and patient injury severity. Experiments on METR-LA and PEMS-BAY demonstrate improved prediction accuracy, better-calibrated uncertainty estimates, and more clinically informed adaptive routing than strong baselines. The proposed framework provides a practical and reliable solution for safety-critical emergency transportation in complex urban traffic systems.
Cui et al. (2026) studied this question.