Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting the ability to identify shared behavioral patterns across locations. We propose a multi-view tensor decomposition framework that captures behavioral similarities across three temporal phases: Approach (warning activation to gate lowering), Waiting (gates down to train passage), and Clearance (train passage to gate raising). We analyze railway crossing videos from multiple locations using TimeSformer embeddings to represent each phase. By constructing phase-specific similarity matrices and applying non-negative symmetric CP decomposition, we discover latent behavioral components with distinct temporal signatures. Our tensor analysis reveals that crossing location appears to be a stronger determinant of behavior patterns than time of day, and that approach-phase behavior provides particularly discriminative signatures. Visualization of the learned component space confirms location-based clustering, with certain crossings forming distinct behavioral clusters. This automated framework enables scalable pattern discovery across multiple crossings, providing a foundation for grouping locations by behavioral similarity to inform targeted safety interventions.

翻译：铁路道口呈现出复杂的安全挑战，驾驶员行为因地点、时间和条件而异。传统方法对各个道口进行独立分析，限制了识别跨地点共享行为模式的能力。我们提出了一种多视角张量分解框架，该框架能够捕捉跨越三个时间阶段的行为相似性：接近阶段（警示激活至道闸下降）、等待阶段（道闸放下至列车通过）和清空阶段（列车通过至道闸升起）。我们使用TimeSformer嵌入对来自多个地点的铁路道口视频进行分析，以表征每个阶段。通过构建特定阶段的相似性矩阵并应用非负对称CP分解，我们发现了具有不同时间特征的潜在行为成分。我们的张量分析表明，道口地点似乎是比一天中的时间更强的行为模式决定因素，并且接近阶段的行为提供了尤其具有区分性的特征。对学习到的成分空间的可视化证实了基于地点的聚类，某些道口形成了独特的行为簇。这一自动化框架能够实现跨多个道口的可扩展模式发现，为按行为相似性对地点进行分组提供了基础，从而为针对性的安全干预措施提供依据。