A Digital Twin Approach for Crowd Flow Modeling on Railway Station Platforms
IEEE International Conference on Smart Computing (SMARTCOMP), pp. 82-89
DOI: 10.1109/SMARTCOMP65954.2025.00069
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.
鉄道駅プラットフォームにおける効果的な群衆追跡は，乗客の安全確保と歩行者流の最適化に不可欠です．特に高密度な都市交通ハブでは，従来の物体検出やマルチオブジェクトトラッキング手法は，深刻なオクルージョンや人物の重なりにより限界があります．
本研究では，深層学習に基づく動き解析と群衆シミュレーションを組み合わせた，駅プラットフォーム上の歩行者流モデリングの新しいアプローチを提案します．最先端のオプティカルフローモデルRAFTを用いてピクセルレベルの動きベクトルを抽出し，クラスタリングによって人の移動パターンを特定します．
リアルな群衆ダイナミクスをシミュレートするため，UnityのNavMeshと拡張焼きなまし法を組み合わせ，高精度のOD（起終点）データを生成します．実世界のビジョン分析をバーチャルワールドモデルに投影し，歩行者流を再現する新しいデジタルツインコンセプトです．