Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians

被引:3
|
作者
Guo, Manze [1 ]
Yuan, Zhenzhou [1 ]
Janson, Bruce [2 ]
Peng, Yongxin [1 ]
Yue, Rui [1 ]
Zhang, Guowu [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Key Lab Transport Ind Big Data Applicat Technol, Minist Transport, POB 173364, Beijing 100044, Peoples R China
[2] Univ Colorado, Dept Civil Engn, Denver, CO 80202 USA
基金
北京市自然科学基金;
关键词
Pedestrian safety; older pedestrian crash; structural equation modeling; factor analysis; CONTRIBUTING FACTORS; INJURY RISK; SAFETY; ENVIRONMENTS; PREDICTORS; FATALITIES;
D O I
10.1080/15389588.2023.2183080
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Objective: 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. Methods: 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. Results: In total, drivers' risky driving behavior (0.270, p<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, p<0.05) is the most critical factor promoting crash severity in high-exposure areas. The natural environment (0.634, p<0.05) in high-exposure areas positively influences older pedestrians' lack of risk perception more than in low-exposure areas. Conclusions: Significant group differences (p-values similar to 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.
引用
下载
收藏
页码:321 / 330
页数:10
相关论文
共 1 条
  • [1] How does street environment affect pedestrian crash risks? A link-level analysis using street view image-based pedestrian exposure measurement
    Hu, Yijia
    Chen, Long
    Zhao, Zhan
    ACCIDENT ANALYSIS AND PREVENTION, 2024, 205