A Digital Twin Approach for Crowd Flow Modeling on Railway Station Platforms

Abstract

Effective crowd tracking at railway station platforms is essential for ensuring passenger safety and optimizing pedestrian flow, particularly in high-density urban transit hubs. However, traditional tracking methods, such as object detection and multi-object tracking, face limitations in congested environments due to severe occlusions and overlapping individuals. This paper proposes a novel approach for modeling pedestrian flow on train station platforms by coupling deep learning-based motion analysis with crowd simulation. In the proposed method, we utilize RAFT, a state-of-the-art optical flow model, to extract pixel-level motion vectors, which are clustered to identify human movement patterns. These motion data are mapped onto a calibrated 2D platform model, providing a top-down representation of pedestrian trajectories. To simulate realistic crowd dynamics, Unity's NavMesh is employed alongside an enhanced Simulated Annealing approach to generate high-accuracy origin-destination (OD) data. This is a new digital twin concept where the analysis from the vision in the real world is projected onto the virtual world model to simulate and reproduce the pedestrian flows. The proposed method was evaluated using synthetic crowd simulation data, demonstrating high accuracy in destination estimation. The experimental results indicate that the OD estimation outperforms conventional approaches, with error rates reduced to half of those observed in YOLOv8x-based tracking systems. These findings suggest that the integration of optical flow-based motion analysis with digital twin simulation can significantly enhance crowd monitoring and congestion management in railway stations.