Unveiling the determinants of injury severities across age groups and time: A deep dive into the unobserved heterogeneity among pedestrian crashes

被引:2
|
作者
Liu, Qingli [1 ]
Li, Fan [1 ]
Ng, Kam K. H. [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong 999077, Peoples R China
关键词
Pedestrian injury severity; Age difference; Random parameters logit model; Unobserved heterogeneity; Partially constrained temporal stability; VEHICLE CRASHES; DIAGNOSTIC-ANALYSIS; MODEL; DRIVER;
D O I
10.1016/j.amar.2024.100336
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Pedestrians, particularly susceptible to road traffic crashes, experience varying injury severities influenced by age and time shifts. This research aims to investigate the differences and temporal shifts in factors influencing pedestrian injury severities across different age groups. To achieve this, three random parameters binary logit models with heterogeneity in the means (and variances) were employed. Four years of pedestrian crash data in Hong Kong were utilized in this study. According to United Nations' definitions of the young and elderly, pedestrians were categorized into three groups: young (under 25 years old), middle-aged (25-65 years old), and elderly (over 65 years old). Initial likelihood ratio tests indicated temporal stability in the young group between 2019 and 2021, with further tests confirming age transferability and overall temporal stability after integrating the three years of young data. The partially constrained temporal stability approach was then developed to further capture the temporal stability of individual variables and simplify model results. Model results identified factors impacting pedestrian injury severities, encompassing pedestrian, driver, vehicle, temporal, and light condition characteristics. Some contributing variables exhibit age-transferability or temporal stability, such as controlled crossing, near controlled crossing, inattentive driver and private car. However, the significance of most contributors varies across age groups and years, with certain factors being age-specific or year-specific. Out-of-sample predictions underscore the cumulative likelihood of fatal or severe injuries with advancing age, and the middle-aged models showed the highest level of temporal stability regarding the risk of injury severity compared to the other two age models. Moreover, middle-aged pedestrians in Hong Kong faced the highest risk of fatal or severe injuries during the first year of the COVID-19 lockdown (2020), but the risk significantly declined for pedestrians of all age groups in the subsequent year. Based on these findings, targeted preventive measures that take into account age differences have been proposed to effectively enhance pedestrian safety.
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页数:26
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