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.
Highlights
With the increase of vehicle miles/kilometers travelled, traffic crash has become one of the main factors that cause human injury and death along with huge property damage
The remainder of this paper is organized as follows: in Section 2, the random parameter bivariate ordered probit model adopted in this paper is introduced in detail; in Section 3, a brief description of the rear-end crashes is provided; Section 4 presents the results and discussion of the model; Section 5 provides the summaries and conclusions of the work
Rear-end crashes have become one of the main factors of human injury along with huge property damage; it is necessary to figure out the possible cause of such kind of traffic accidents
Summary
With the increase of vehicle miles/kilometers travelled, traffic crash has become one of the main factors that cause human injury and death along with huge property damage. As pointed out by Chen et al [9], these naturally collected data usually include almost all crash-related perspectives like human factors (age, seat position, sex, fatigue, alcohol usage, and so on), vehicle (speed, vehicle type, weight, and so on), and environment (light condition, road surface, weather, and so on). These data reflect the real state of crashes pretty well. Yuan et al developed a binary logistic regression model to predict occupant injury severity This model identified corresponding affecting factors in rear-end crashes involving trucks as the front vehicle [14]. The remainder of this paper is organized as follows: in Section 2, the random parameter bivariate ordered probit model adopted in this paper is introduced in detail; in Section 3, a brief description of the rear-end crashes is provided; Section 4 presents the results and discussion of the model; Section 5 provides the summaries and conclusions of the work
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