Pedestrians are a vulnerable road user group, and assessing their crash risk at critical locations, such as signalized intersections, is crucial for developing targeted countermeasures. While conflict-based safety assessments using traffic conflict measures effectively estimate crash risk, they often overlook the heterogeneity of different motorized and non-motorized road users. Conversely, field-based theories account for road user heterogeneity, yet their application in crash risk assessment, specifically evaluating pedestrian crash risk, and particularly by severity level using real-world data, remains underexplored. This study introduces a novel application of physics-informed risk force theory for assessing pedestrian crash risk by injury severity, utilizing facility-based video data at signalized intersections. The study derives risk forces that encompass pedestrian and vehicle heterogeneity as a nearness-to-collision component and vehicle impact speed as a severity component. Stationary and non-stationary extreme value models, incorporating exogenous traffic parameters at the signal cycle level, were applied to 72 h of video data collected from three signalized intersections in Queensland, Australia. The non-stationary univariate extreme value model with risk force as a measure of nearness-to-collision reliably estimated total crash frequency compared to historical crash records. In addition, the bivariate extreme value model with risk force and impact speed reasonably predicted pedestrian crashes by severity levels. The results also indicate that an increased volume of interacting pedestrians and left-turning vehicles elevates the likelihood of total and severe crashes. The proposed pedestrian crash risk assessment framework offers a unified and efficient proactive approach that can enhance automated safety analysis of traffic facilities, thereby assisting road authorities in real-time safety management.

中文翻译：

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一种基于物理学的风险力理论，用于使用基于人工智能的视频分析按严重程度估计行人碰撞风险

行人是易受攻击的道路使用者群体，评估他们在关键位置（例如信号灯交叉路口）的碰撞风险对于制定有针对性的对策至关重要。虽然使用交通冲突措施的基于冲突的安全评估可以有效地估计碰撞风险，但它们往往忽视了不同机动和非机动道路使用者的异质性。相反，基于现场的理论解释了道路使用者的异质性，但它们在碰撞风险评估中的应用，特别是评估行人碰撞风险，特别是使用真实世界数据按严重程度评估，仍然没有得到充分探索。本研究介绍了物理知情风险力理论的新应用，用于利用信号交叉路口基于设施的视频数据，通过伤害严重程度评估行人碰撞风险。该研究得出的风险力包括行人和车辆的异质性作为接近碰撞的组成部分，以及车辆碰撞速度作为严重性组成部分。将稳态和非平稳极值模型，结合信号周期级别的外生交通参数，应用于从澳大利亚昆士兰州三个信号灯交叉口收集的 72 h 视频数据。非平稳单变量极值模型以风险力作为碰撞接近度的度量，与历史碰撞记录相比，可靠地估计了总碰撞频率。此外，具有风险力和冲击速度的二元极值模型按严重程度合理预测行人碰撞。结果还表明，互动的行人和左转车辆数量的增加增加了发生完全和严重碰撞的可能性。 拟议的行人碰撞风险评估框架提供了一种统一且高效的主动方法，可以加强对交通设施的自动安全分析，从而协助道路当局进行实时安全管理。