An analysis of crashes occurring in 252 unidirectional Italian motorway tunnels over a 4-year monitoring period is provided to identify the main causes of crashes in tunnels. In this paper, we propose a full Bayesian bivariate Poisson lognormal hierarchical model with correlated parameters for the joint analysis of crashes of two levels of severity, namely severe (including fatality and injury accidents only) and non-severe (property damage only), providing better insight on the available data with respect to an analysis based on severe and non-severe independent univariate models. In particular, the proposed model shows that for both of severity levels the crash frequency increases with some parameters: the average annual daily traffic per lane, the tunnel length, and the percentage of trucks, while the presence of the sidewalk provides a reduction in severe accidents. Also the presence of the third lane induces a reduction in severe accidents. Moreover, a reduction in the crash frequency of the two crash-types over years is present. The correlation between the parameters might offer additional insights into how some combinations can affect safety in tunnels. The results are critically discussed by highlighting strength and weakness of the proposed methodology.

提供了对为期4年的监控期间意大利252条单向高速公路隧道中发生的撞车事故的分析，以确定隧道中撞车的主要原因。在本文中，我们提出了一个具有相关参数的完整贝叶斯双变量Poisson对数正态分层模型，用于联合分析两个严重性级别的崩溃，即严重（仅包括死亡和伤害事故）和非严重（仅包括财产损失），提供相对于基于严重和非严重独立单变量模型的分析，可以更好地了解可用数据。尤其是，所提出的模型表明，对于两种严重性级别，碰撞频率都会随着某些参数的增加而增加：每条车道的年平均每日通行量，隧道长度和卡车的百分比，人行道的存在减少了严重事故的发生。同样，第三车道的存在导致严重事故的减少。此外，存在两种碰撞类型的碰撞频率随时间降低的情况。参数之间的相关性可能会为某些组合如何影响隧道安全性提供更多的见解。通过突出提出的方法的优势和劣势，对结果进行了严格的讨论。