Guo Manze, Yuan Zhenzhou, Janson Bruce, Peng Yongxin, Yue Rui, Zhang Guowu
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China.
Department of Civil Engineering, University of Colorado Denver, Denver, Colorado, USA.
Traffic Inj Prev. 2023;24(4):321-330. doi: 10.1080/15389588.2023.2183080. Epub 2023 Mar 29.
Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between factors in specific exposure areas is the first step to improving safety. Therefore, examining the causal factors' impact on pedestrian-vehicle crash severity in a given area is vital for formulating effective measures to reduce the risk of pedestrian fatalities and injuries.
This study implements the Thiessen polygon algorithm deployed to define older pedestrians' exposure influence area. Enabling trip characteristics and built environment information as exposure index settings for the background of the pedestrian severity causal analysis. Then, structural equation modeling (SEM) was applied to conduct a factor analysis of the crash severity in high- and low-exposure areas. The SEM evaluates latent factors such as driver risk attitude, risky driving behavior, lack of risk perception among older pedestrians, natural environment, adverse road conditions for driving or walking, and vehicle conditions. The SEM crash model also establishes the relationship between each latent factor.
In total, drivers' risky driving behavior (0.270, < 0.05) in low-exposure areas significantly impacts older pedestrian crash severity more than in high-exposure areas. Lack of risk perception among older pedestrians (0.232, < 0.05) is the most critical factor promoting crash severity in high-exposure areas. The natural environment (0.634, < 0.05) in high-exposure areas positively influences older pedestrians' lack of risk perception more than in low-exposure areas.
Significant group differences (p-values ∼ 0.001-0.049) existed between the causal factors of the high-exposure risk areas and the low-exposure risk factors. Different exposure intervals require detailed scenarios based on the critical risks identified. The crash severity promotion measures in different exposure areas can be focused on according to the critical causes analyzed. Those clues, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs toward improving the safety and mobility of older pedestrians.
老年行人在交通事故中更有可能遭受严重后果或死亡。识别导致事故严重程度的因素以及特定暴露区域内各因素之间可能存在的相互依存关系，是提高安全性的第一步。因此，研究给定区域内因果因素对行人与车辆碰撞严重程度的影响，对于制定有效措施以降低行人伤亡风险至关重要。
本研究采用泰森多边形算法来定义老年行人的暴露影响区域。将出行特征和建成环境信息作为行人严重程度因果分析背景下的暴露指标设置。然后，应用结构方程模型（SEM）对高暴露区域和低暴露区域的碰撞严重程度进行因素分析。SEM评估潜在因素，如驾驶员风险态度、危险驾驶行为、老年行人缺乏风险感知、自然环境、不利于驾驶或行走的道路状况以及车辆状况。SEM碰撞模型还建立了各潜在因素之间的关系。
总体而言，低暴露区域驾驶员的危险驾驶行为（0.270，<0.05）对老年行人碰撞严重程度的影响比高暴露区域更大。老年行人缺乏风险感知（0.232，<0.05）是高暴露区域碰撞严重程度上升的最关键因素。高暴露区域的自然环境（0.634，<0.05）对老年行人缺乏风险感知的正向影响比低暴露区域更大。
高暴露风险区域和低暴露风险区域的因果因素之间存在显著的组间差异（p值约为0.001 - 0.049）。不同的暴露区间需要根据识别出的关键风险制定详细的场景。可以根据分析出的关键原因，针对不同暴露区域的碰撞严重程度提升措施进行重点关注。反过来，这些线索可供交通管理部门在确定其改善老年行人安全性和出行便利性的计划、政策及项目的优先级时使用。
