TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosis

Diagnosing collective anomalies from urban trajectories is increasingly important for traffic governance, as it reveals what happened, who was involved, and where and when the event occurred. Existing detectors efficiently produce scores or labels, whereas vision--language pipelines provide richer semantics; neither couples verifiable diagnosis with low-latency monitoring. The central challenge is to recognize collective patterns and recover exact event details from the source trajectories without running the full diagnostic pipeline for every monitored window. We therefore separate always-on screening from on-demand diagnosis: screening raises alerts, while diagnosis releases only source-verified what--who--where--when records. We present TrajMind, a fast-and-slow framework that switches three role-specialized LoRA adapters over one frozen vision--language backbone. Its slow path, textitTrajMindslow, chains canvas-based typing, type-conditioned localization over serialized trajectories, and executable verification, yielding structured, evidence-backed diagnoses. Additionally, the fast path, textitTrajMindfast, screens each window in a single text-only pass, delivering efficient structured alerts. Extensive experiments show that, TrajMindslow outperforms the strongest baselines by at least 15.3 percentage points in anomaly typing and 13.8 percentage points in localization. These gains persist under cross-city transfer, and TrajMindfast reduces latency by 41.1% and maintains binary balanced accuracy of at least 93.5%. Together, TrajMind delivers accurate, evidence-backed diagnoses across cities and efficient front-line monitoring.