It is acknowledged that crash occurrence on segments and intersections could be affected by multilevel factors. Omission of important explanatory variables could result in biased and inconsistent parameter estimates. This paper contributes to the literature by examining the zonal effects which are always excluded or ignored in traffic safety research for segments and intersections. A grouped random parameters multivariate spatial model is proposed to identify both observable zonal effects and unobserved heterogeneity at the zonal level by considering the heterogeneous and spatial correlations. The proposed model is evaluated by comparing it with its three counterparts: a fixed parameters univariate spatial model without zonal factors, a random parameters univariate spatial model without zonal factors, and a random parameters univariate spatial model with zonal factors. The results indicate that the three random parameters models could consistently provide better performance than the fixed parameters model and the models including zonal factors outperform the models without zonal factors. Besides, the proposed model has the optimal model performance compared with its counterparts, which validates the concept of adopting the multivariate modeling framework to identify the heterogeneous and spatial correlations of zonal effects. The results confirm the significantly correlated heterogeneous residuals in modeling zonal factors on crash occurrence on segments and intersections. However, the spatial correlations of zonal effects on different types of road entities (segments and intersections) in adjacent zones are not statistically significant. Furthermore, the proposed model provides more valuable insights about the crash occurrence on segments and intersections by revealing segment-/intersection-level factors together with zonal factors.

公认的是，路段和交叉路口的撞车事故可能会受到多级因素的影响。省略重要的解释变量可能会导致参数估计有偏差和不一致。本文通过研究在路段和交叉口的交通安全研究中始终被排除或忽略的地带效应为文献做出了贡献。提出了一个分组的随机参数多元空间模型，通过考虑异质性和空间相关性，在区域水平上识别出可观察到的区域效应和未观察到的异质性。将该模型与以下三个模型进行比较，以评估该模型：固定参数单变量空间模型（不包含区域因子），随机参数单变量空间模型（不包含区域因子），以及带有区域因子的随机参数单变量空间模型。结果表明，三个随机参数模型可以比固定参数模型始终提供更好的性能，并且包含区域因子的模型要优于没有区域因子的模型。此外，所提出的模型与同类模型相比具有最佳的模型性能，这验证了采用多元建模框架来识别区域效应的异质和空间相关性的概念。结果证实，在对区域和交叉路口发生碰撞的区域因子进行建模时，异质残差具有显着相关性。但是，相邻区域中不同类型道路实体（路段和交叉路口）上的区域效应的空间相关性在统计上并不显着。