考虑多模式失效概率的长下坡路段重型卡车事故预测模型

为挖掘多模式失效概率与长下坡路段重型卡车事故之间的关系，建立了重型卡车在长下坡路段的多模式失效概率与车辆事故之间的关系模型。并针对重型卡车在长下坡路段可能的失效模式，如侧滑、侧翻、视距不足、制动失效，在此基础上建立了多模式失效概率预测模型；通过蒙特卡罗法模拟并求解单模式失效的概率，宽界限法求解失效系统的多模式失效概率；将多模式失效概率作为解释变量与其他道路因素结合，分别建立泊松模型、随机效应泊松模型、随机参数泊松模型，将多模式失效概率与重型卡车事故建立函数关系；对比3种模型的拟合优度指标，优选出最优事故预测模型，用来挖掘重型卡车事故与多模式失效概率之间的关系。以华盛顿州71段长下坡10年的重型卡车事故数据及道路设计数据进行方法验证。结果表明:随机参数泊松模型与随机效应泊松模型的拟合优度相差较小，二者均优于泊松模型；当考虑多模式失效概率时，平曲线半径、纵坡坡度、超高对重型卡车事故的影响均不显著，即三者的影响被削弱，尤其是平曲线半径和超高，多模式失效概率的弹性(0.239)远大于二者的弹性(平曲线半径和超高的弹性分别仅为0.097和0.002)；重型卡车的事故与多模式失效概率近似线性关系，且截距不为0。即多模式失效概率可用于道路安全分析的表征指标，但与事故概率不等价。 

Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability

A crash prediction model is developed, in order to explore the relationship between multi-mode failure probability and heavy-duty truck crashes over long downhill road sections. A model for multi-mode failure probability prediction is developed to study the probability of different types of failures associated with heavy-duty trucks, such as skidding, rollover, insufficient sight distance, and braking failure, on the long downhill sections. The single-mode failure probability is simulated using a Monte Carlo method and the multi-mode failure probability of the system is studied by a wide bound method. Three crash prediction models including a Poisson model, a random-effect Poisson model, and a random-parameter Poisson model are developed, considering multi-mode failure probability as one of the explanatory variables together with other impact factors. The models are used to link the multi-mode failure probability with the crashes of heavy-duty trucks. The optimal crash prediction model is selected through the goodness-of-fit for accurately modeling the relationship between crashes of the trucks and their multi-mode failure probability. The method is verified by a 10-year data of heavy-duty truck crash and road design of 71 long downhill sections in the Washington State, the United States. The results show that there is little difference in the goodness of fit between the random-effect Poisson model and random-parameter Poisson model, and both of them are better than the Poisson model. It is found that radius of the horizontal curves, grades and superelevation rates are not significant in leading to the crashes, when compared with the multi-mode failure probability. Study results show that, the elasticity of multi-mode failure probability (0.239) is much greater than that of the radius of horizontal curve and superelevation (0.097 and 0.002) respectively; heavy-duty truck crashes and multi-mode failure probability are approximately linearly correlated, and the intercept of the model is found to be other than "0". The above results indicate that the multi-mode failure probability can be used for road safety analysis, but it is not equivalent to the crash probability, which may be used as a basis for improving road design.