Transit Origin-Destination (OD) data are fundamental for optimizing public transit services, yet current collection methods, such as manual surveys, Bluetooth/WiFi tracking, and Automated Passenger Counters, are often costly, device-dependent, or unable to support individual-level matching. Meanwhile, onboard surveillance cameras already deployed on most transit vehicles provide an underutilized opportunity for automated OD data collection. Leveraging this, we present TransitReID, a framework for individual-level and occlusion-resistant passenger re-identification (ReID) tailored to transit environments. TransitReID introduces three key components: (1) an occlusion- and viewpoint-robust ReID algorithm that integrates a variational autoencoder-guided region-attention mechanism with selective feature pooling to emphasize visible and discriminative body regions; (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism that adapts static ReID matching to dynamic bus operations while balancing accuracy, memory, and speed; and (3) a multi-threaded edge implementation that enables near real-time OD estimation while preserving privacy through local data processing. We also construct a new Transit ReID dataset with over 17,000 images captured from real bus front/rear cameras under diverse occlusion and viewpoint conditions. Experimental results show that TransitReID achieves state-of-the-art ReID performance, attaining 88.3% R-1 accuracy on the proposed transit ReID dataset and sustaining 80-90% OD estimation accuracy in both simulations and real-world operation, with deployment supported on NVIDIA Jetson edge devices. This work provides an algorithmic and system-level foundation for scalable, privacy-preserving automated transit OD collection.

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