Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

Abstract

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

Job stress and emotional exhaustion at work in Spanish workers: Does unhealthy work affect the decision to drive?

“…In this regard, recent evidence shows that there are different patterns of involvement in traffic accidents and penalties concerning groups of professional and non-professional drivers, related to stress factors and risky behaviors such as speeding, being aggressive towards other road users, and driving under fatigue-related conditions [60,61]. Indeed, recent researches have documented the actual influence of factors such as fatigue and driver's demographic features [59] on both risky behaviors performed and severity of traffic crashes suffered [37,42,46,62]. Furthermore, the relative risk of accidents caused by human factors is higher among professional drivers, but other occupations that present various factors related to job strain have highly odd ratios of being involved in road accidents when driving as well [61].…”

“…Nevertheless, different systematic actions such as the assessment of tasks, a constant monitoring of workers' mental health, the management of fatigue [26,42] and the increase and enhancement of peers' social support, may strengthen the prevention and management of both job stress and burnout-related symptoms at the workplace [43,44]. The latter acquires special relevance when considering that, according to findings from other empirical studies, emotional exhaustion and its related psychosocial outcomes at work not only seem to affect the work-related performance, but -and as we have mentioned when addressing the findings of Li et al [10]-to influence the workers' outcomes in further spheres, such as decision making and safe behaviors at the wheel.…”

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

“…This study adopted a random-parameters ordered probit approach to provide a deep understanding of the influence of contributing factors on occupant injury. This model considers the ordinal nature of injury data, and it is statistically superior to the fixed parameters ordered probit model as it accounts for possible unobserved factors [7][8][9]. The findings of this study will shed light on identifying the potential difference of contributing factors between truck as leading vehicle and passenger-car as the leading vehicle in rear-end collisions, therefore guide making tailored countermeasures to alleviate the resulting injury severity levels.…”

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

“…ere is a significant difference in the distribution of the gaze points under mental load levels. Specifically, as the mental load increases, the concentrated area of the gaze points will shrink [18].…”