Abstract
Transit Origin-Destination (OD) data are essential for transit planning, particularly in route optimization and demand-responsive paratransit systems. Traditional methods, such as manual surveys, are costly and inefficient, while Bluetooth and WiFi-based approaches require passengers to carry specific devices, limiting data coverage. On the other hand, most transit vehicles are equipped with onboard cameras for surveillance, offering an opportunity to repurpose them for edge-based OD data collection through visual person re-identification (ReID). However, such approaches face significant challenges, including severe occlusion and viewpoint variations in transit environments, which greatly reduce matching accuracy and hinder their adoption. Moreover, designing effective algorithms that can operate efficiently on edge devices remains an open challenge. To address these challenges, we propose TransitReID, a novel framework for individual-level transit OD data collection. TransitReID consists of two key components: (1) An occlusion-robust ReID algorithm featuring a variational autoencoder guided region-attention mechanism that adaptively focuses on visible body regions through reconstruction loss-optimized weight allocation; and (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism specifically designed for efficient and robust transit OD matching which balances storage, speed, and accuracy. Additionally, a multi-threaded design supports near real-time operation on edge devices, which also ensuring privacy protection. We also introduce a ReID dataset tailored for complex bus environments to address the lack of relevant training data. Experimental results demonstrate that TransitReID achieves state-of-the-art performance in ReID tasks, with an accuracy of approximately 90\% in bus route simulations.
Abstract (translated)
翻译如下： 转换起点-终点（OD）数据对于公共交通规划至关重要，特别是在路线优化和需求响应型辅助交通系统中。传统的手动调查方法成本高且效率低下，而基于蓝牙和WiFi的方法则需要乘客携带特定设备，从而限制了数据的覆盖面。相比之下，大多数公共交通工具都配备了用于监控的车载摄像头，这为通过视觉行人再识别（ReID）重新利用这些设备来收集边端OD数据提供了机会。然而，这种方法面临着严重的挑战，包括在公共交通环境中出现严重遮挡和视角变化的问题，这些问题大大降低了匹配准确性，并阻碍了其采用。此外，在边缘设备上运行的高效算法的设计仍然是一个开放性的难题。为了解决这些挑战，我们提出了TransitReID，这是一个用于个人级别公共交通OD数据收集的新框架。TransitReID包含两个关键组件：（1）一种鲁棒于遮挡的ReID算法，该算法结合了变分自编码器指导下的区域注意力机制，通过优化重建损失权重分配来适应性地关注可见的身体部位；（2）一个专门为高效和稳健的公共交通OD匹配设计的层次化存储与动态匹配（HSDM）机制，该机制平衡了存储、速度和准确性的需求。此外，一个多线程的设计支持边缘设备上的近实时操作，并确保隐私保护。我们还引入了一个专为复杂公交环境定制的ReID数据集，以解决相关训练数据不足的问题。实验结果表明，TransitReID在ReID任务中达到了最先进的性能，在公交车路线模拟中的准确率约为90%。
URL
https://arxiv.org/abs/2504.11500
