TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification

Abstract--Transit Origin-Destination (OD) data are fundamental for optimizing public transit services, yet current collection methods, such as manual surveys, Bluetooth/WiFi tracking, or Automated Passenger Counters, are either costly, device-dependent, or incapable of 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 novel framework for individual-level and occlusion-resistant passenger re-identification (ReID) tailored to transit environments. Our approach introduces three key innovations: (1) an occlusion-robust ReID algorithm that integrates a variational autoencoder-guided region-attention mechanism and selective quality feature averaging to dynamically emphasize visible and discriminative body regions under severe occlusions and viewpoint variations; (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism that transforms static gallery matching into a dynamic process, balancing accuracy, memory, and speed in real-world bus operations; and (3) a multi-threaded edge implementation that enables near real-time OD estimation while ensuring privacy by processing all data locally. T o support research in this domain, we also construct a new Transit ReID dataset with over 17,000 images captured from bus front/rear cameras under diverse occlusion and viewpoint conditions. Experimental results demonstrate that TransitReID achieves state-of-the-art performance, with R-1 accuracy of 88.3% and mAP of 92.5%, and further sustains 90% OD estimation accuracy in bus route simulations on NVIDIA Jetson edge devices. This work advances both the algorithmic and system-level foundations of automated transit OD collection, paving the way for scalable, privacy-preserving deployment in intelligent transportation systems. RANSIT Origin-Destination (OD) data collection plays a critical role in the fields of transportation engineering for urban planning, service frequency adjustments, and route optimization. It aids in the analysis of relationships and impacts among regional economic development, infrastructure, and urban mobility [1]. For public transportation systems, OD data represents passenger demand and plays a crucial role in designing routes more efficiently to minimize travel time, reduce operating costs, and optimize driver scheduling. As shown in Figure 1, traditional methods for estimating transit OD data rely on manual approaches such as surveys, which are labor-intensive and suffer from low response rates [2]. More advanced methods, such as those utilizing mobile phone data [3] and Bluetooth technology [4], require passengers to carry specific devices with WiFi or Bluetooth enabled, which limits coverage and might raise privacy concerns due to the unique identifiers of the device.