Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

中文翻译：

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随机参数多元负二项式回归，用于通过碰撞类型模拟影响因素对碰撞频率的影响

高速公路是安全高效驾驶的基础。道路几何形状在动态驾驶系统中起着至关重要的作用。诸如飞机，纵向路线，交通量以及驾驶员视线特征之类的因素决定了汽车和卡车的安全运行速度。反过来，运行速度会影响高速公路上撞车的频率和类型。方法。独立的负二项式和泊松模型被认为是本研究建模的基础方法。但是，随机参数模型减少了未观察到的异质性并获得了更高的尺寸。因此，我们提出了随机参数多元负二项式（RPMNB）模型，以分析交通，速度，道路几何形状和视觉特征对追尾，碰撞护栏，其他事故，伤亡事故的影响。随后，我们计算拟合优度和预测性措施以确认所提出模型的优越性。最后，我们还计算弹性效应以扩大比较。结果。在显着变量中，黑点，年平均每日交通量（AADT），汽车的行驶速度，汽车的速度差以及当前平面曲线的长度对碰撞风险具有积极影响，而卡车的速度差，车辆的长度纵向坡度对应于最小坡度，并且停止视线距离会对撞车风险产生负面影响。根据结果​​，可以采取几种切实有效的措施来提高道路设计和运营过程中的安全性。此外，拟合优度和预测性措施显然凸显了RPMNB模型与标准模型相比的更高性能。所有模型的弹性效应均显示出与RPMNB模型相当的性能。从而，