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.

For video-based person Re-Identification (Re-ID), how to efficiently extract temporal motion features and spatial appearance features from video sequences is a key issue. Conventional approaches focus on modelling the entire video spatio-temporal features, ignoring the inherent differences between temporal motion features (e.g., gait) that change over time and spatial appearance features (e.g., clothing) that are stable over time in terms of attributes. Because of their different sensitivities in real-world scenarios, conventional approaches often lose critical fine-grained features. To address these issues, we propose a T emporal M otion and spatial E nhanced A ppearance with T ransformer-based (T 2 MEA) framework for modelling spatial–temporal video discriminative representations. Specifically, (1) Dual-Branch Architecture: The content branch emphasises extracting the overall structure of the video using the spatial–temporal aggregation (STA) module from a global view, whereas the fovea branch focuses on gaining local fine-grained spatio-temporal features. (2) Zero-Parameter Design: the [CLS] Token Channel Shift Interaction (TCSI) module captures the dynamic features and static features between adjacent frames without additional parameters; the Spatial Patches Shift Enhancing (SPSE) module is introduced to enhance appearance features within frame to address occlusion and illumination changes without additional parameters. (3) Spatial–Temporal Interaction: The Cross-Attention Aggregation (CAA) module is proposed to interact between temporal and spatial features and further enrich the spatial–temporal feature representation for video sequences. Extensive experiments on three public Re-ID benchmarks (MARS, iLIDS-VID, and PRID-2011) demonstrate that the proposed framework outperforms several state-of-the-art methods.