Many studies have been devoted to investigate the spatial variations (heterogeneities) in the effects of risk factors on crash likelihood. However, investigations mainly target the safety effects on the mean of the crash data (distribution). Less attention was paid to investigate the spatial nonstationary effects on the different quantiles of the crash data distribution. In this study, a conditional quantile-based Bayesian hierarchical random parameter Tobit model is proposed to investigate the regional varying effects of road-related factors on crash rate at different quantiles of the crash rate distribution. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road related data for a three-year period is used for a case study. The results show that: 1) the regression coefficients of all of the selected risk factors vary over a wide range among 34 counties on every investigated quantiles of crash rate distribution; 2) in each county, the regression coefficients of all of the factors vary over investigated quantiles of the crash rate distribution, and for the same factor, the coefficients present different ranges of the variation in different counties; 3) the 50th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model outperforms the conditional mean-based Bayesian hierarchical random parameter Tobit model, Bayesian quantile Tobit model and Bayesian Tobit model in terms of the prediction accuracy measured by the MAE, and 75th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model is outstanding in terms of the goodness-of-fit measured by the DIC. These findings suggest the importance of investigating the regional nonstationary effects of risk factors for different quantiles of the crash rate distribution. The practical implications of the proposed conditional quantile-based Bayesian hierarchical random parameter Tobit model in terms of data prediction, parameters interpretation and safety effects explanation are highlighted at the end of this paper.

许多研究致力于研究危险因素对撞车可能性的影响中的空间变化（异质性）。但是，调查主要针对安全性影响的平均碰撞数据（分布）。人们很少关注研究空间非平稳效应对碰撞数据分布的不同分位数的影响。在这项研究中，基于条件分位数的贝叶斯分层随机参数Tobit模型建议研究道路相关因素在碰撞率分布的不同分位数下对碰撞率的区域变化影响。案例研究使用了一种特定的道路设施类型，即佛罗里达州的城市两车道两路道路网段，以及三年期间的碰撞和道路相关数据。结果表明：1）在每个调查的崩溃率分布的分位数上，所有选择的风险因素的回归系数在34个县中的很大范围内变化；2）在每个县，所有因素的回归系数在所调查的崩溃率分布的分位数上都不同，对于同一因素，该系数在不同县中的变化范围不同；3）在基于MAE测得的预测精度方面，基于第50位数的基于条件分位数的贝叶斯分层随机参数Tobit模型优于基于条件的基于均值的贝叶斯分层随机参数Tobit模型，贝叶斯分位数Tobit模型和贝叶斯Tobit模型以及第75位数的条件基于分位数的贝叶斯分层随机参数Tobit模型在DIC测量的拟合优度方面非常出色。这些发现表明，调查崩溃率分布的不同分位数的危险因素的区域非平稳影响的重要性。建议的实际意义最后，重点介绍了基于条件分位数的贝叶斯分层随机参数Tobit模型在数据预测，参数解释和安全效果解释方面的优势。