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Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach

机译:基于相关随机参数的高速公路隧道碰撞频率分析

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The paper provides an analysis of the frequency of total accidents (accidents involving material damage, physical injuries and fatalities), which occurred in 226 unidirectional motorway tunnels over a four-year monitoring period, based on unrelated and correlated random-parameter models. The so-called random-intercept model, in which only the regression intercept is assumed to be random, was also developed a priori for recording the random-effects (temporal correlations among accidents occurring in the same tunnel in different years). The independent variables were: tunnel length (L), annual average daily traffic (AADT) per lane, percentage of trucks (%Tr), presence of a sidewalk (SW), longitudinal slope (LS), and mechanical ventilation (MV). The comparison among the aforementioned three models showed that the correlated random-parameters model, which takes into account the cross correlation among the random-parameters, provided a better goodness-of-fit than the corresponding uncorrelated random-parameter and intercept-random models. This means that more precise estimations of accidents can be obtained when the random-parameters are assumed to be correlated in statistical analysis. The developed model also offers additional insights into showing how different combinations of parameters affect tunnel safety. In particular, through the correlation coefficient matrix of random-parameters, we found that the non-constant longitudinal slope (LS) alleviates the effect of the annual average daily traffic (AADT) per lane on increasing crash frequency. In addition, the presence of the mechanical ventilation (MV) in tunnels makes less significant the influence of AADT per lane on increasing crash frequency, too. The knowledge of these correlations may be useful for future applications, for example, for road engineers in designing tunnel. The model proposed can also be used by Tunnel Management Agencies (TMAs) for estimating possible variations in accident frequency in a specific tunnel due to modifications of traffic control systems.
机译:本文基于不相关和相关的随机参数模型,对在四年的监测期内发生在226条单向高速公路隧道中的总事故(涉及物质损坏,人身伤害和死亡的事故)的发生频率进行了分析。还开发了一种先验先验的随机拦截模型,其中仅假设回归拦截是随机的,以记录随机效应(不同年份同一条隧道中发生的事故之间的时间相关性)。自变量为:隧道长度(L),每条车道的年平均每日通行量(AADT),卡车百分比(%Tr),人行道(SW),纵向坡度(LS)和机械通风(MV)。上述三个模型之间的比较表明,相关的随机参数模型考虑了随机参数之间的互相关性,比相应的不相关的随机参数模型和拦截随机模型具有更好的拟合优度。这意味着,在统计分析中假设随机参数相互关联时,可以获得更精确的事故估计。所开发的模型还提供了更多的见解,可显示出不同的参数组合如何影响隧道安全。特别是,通过随机参数的相关系数矩阵,我们发现非恒定纵向坡度(LS)减轻了每条车道的年平均日行车流量(AADT)对增加的碰撞频率的影响。另外,隧道中机械通风(MV)的存在也使得每车道AADT对增加撞车频率的影响也不太明显。这些相关性的知识可能对将来的应用很有用,例如,对于道路隧道设计中的道路工程师而言。隧道管理机构(TMA)也可以使用提出的模型来估计由于交通控制系统的修改而导致的特定隧道事故频率的可能变化。

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