Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

中文翻译：

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基于POI出行特征和行人暴露强度的时空深度学习行人碰撞风险预测方法

行人是弱势道路使用者群体，他们直接暴露在复杂的交通条件下，从而增加了受伤或死亡的风险。本研究首先构建了一个多维指标来量化行人暴露度，考虑了兴趣点（POI）属性、POI强度、交通量和行人步行能力等因素。经过风险插值和特征工程，形成了全面的风险预测数据源。最后，基于风险因素，综合VGG16深度卷积神经网络和Transformer深度学习网络的算法特点，提出VT-NET深度学习网络模型。该模型涉及分别训练非时间特征和时间特征。训练数据集包含天气条件、暴露强度、社会经济因素和建筑环境等特征。VT-NET模型通过对不同类型的因果特征变量采用不同的训练方法，分析风险特征的变化，并分别训练时间和非时间风险变量。它用于生成跨四个时空尺度的碰撞风险的时空网格级预测。对 VT-NET 模型的性能进行了评估，揭示了其在预测研究区域行人碰撞风险方面的功效。结果表明，碰撞风险集中的区域主要位于市中心，并持续数小时。这些高风险区域会在深夜和清晨消散。高风险区域还集中在市中心；与工作日相比，这种聚集行为在周末更为突出，并且与商业区、公共空间以及教育和医疗设施重合。