Li Yue, Shi Yuanyuan, Xiong Huiyuan, Jian Feng, Yu Xinxin, Sun Shuo, Meng Yunlong
Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China.
Sensors (Basel). 2024 Dec 3;24(23):7720. doi: 10.3390/s24237720.
Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian-vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors "Workday or not" (Not), "Signal control mode" (No signal and Other security facilities), and "Road safety attribute" (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels.
道路交通安全是公共安全的重要组成部分，也是一个具有全球意义的问题。行人作为交通活动的关键参与者，一直是交通安全的主要关注对象。在智能交通系统（ITS）迅速发展的背景下，探索预防行人与车辆碰撞事故中行人死亡的有效策略至关重要。本文旨在基于从多个基于传感器的来源收集的涉及行人的碰撞数据，研究影响行人受伤严重程度的因素。为此，提出了一种基于SHAP方法的随机参数logit模型和随机森林的混合方法。具体而言，随机参数logit模型用于揭示显著因素和参数的随机变异性，而基于SHAP的随机森林用于识别重要影响因素和特征贡献。结果表明，该混合方法不仅可以验证自身，还可以补充更多结论。确定了八个显著影响因素，其中七个因素被随机森林分析确定为重要因素。然而，发现“是否为工作日”（否）、“信号控制模式”（无信号和其他安全设施）和“道路安全属性”（正常道路）等因素并不显著。需要注意的是，仅关注显著或重要因素可能会导致忽略某些结论。所提出的智能交通系统策略有可能显著提高行人安全水平。
