Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation

Accurately estimating urban rail platform occupancy can support more informed operational decision-making by transit agencies, particularly during periods of crowding. However, sensing real-time platform occupancy remains challenging and often relies on indirect proxies, such as automatic fare collection data or staff observations. Recently, Closed-Circuit Television (CCTV) footage has emerged as a promising data source for accurate, real-time occupancy estimation. The present study investigates this approach by comparing three state-of-the-art computer vision approaches for extracting crowd-related features from platform CCTV imagery: (a) object detection and counting using YOLOv11, RT-DETRv2, and APGCC; (b) crowd-level classification via a custom-trained Vision Transformer (Crowd-ViT); and (c) semantic segmentation using DeepLabV3. Additionally, we present a novel and efficient convex ridge regression over mean-pooled segmentation features to extract counts from the generated segmentation maps while accounting for image depth and passenger dispersion along a platform. Tested on a privacy-preserving dataset, created in collaboration with the Washington Metropolitan Area Transit Authority (WMATA), and including more than 600 hours of video material, our results demonstrate that computer vision approaches can provide substantive value for crowd estimation. This work demonstrates that CCTV image data, independent of other data sources available to transit agencies, can enable real-time crowding estimation and, eventually, timely operational responses to mitigate platform crowding.