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首页> 外文会议>International conference of Chinese transportation professionals;ICCTP 2011 >Using Multivariate Poisson-Lognormal Regression Method for Modeling Crash Frequency by Severity on Freeway Diverge Areas
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Using Multivariate Poisson-Lognormal Regression Method for Modeling Crash Frequency by Severity on Freeway Diverge Areas

机译：使用多元泊松-对数正态回归方法通过高速公路扩散区的严重程度对事故发生频率进行建模

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Numerous efforts have been devoted to investigating the relationship between traffic crashes by severity and explanatory factors. However, traditional methods estimate the crash frequency of each severity level separately, neglecting the correlations of severity levels, which can result in biased model estimations. The primary objective of this study is to develop a multivariate Poisson-lognormal regression model (MVPLN), which can simultaneously model crash counts by severity, to evaluate the effects of contributing factors to crash frequency and to identify the correlations among different severity levels. Crash data were collected at 263 exit ramps on freeways in Florida, United States. The Markov chain Monte Carlo (MCMC) method was used to get the estimation solution of MVPLN model. Significant correlations were identified among crash counts at different severity levels. The effects of crash-related factors of each severity level were explored in the MVPLN model considering the correlation structure.
机译：已经进行了许多努力来通过严重程度和解释性因素来研究交通事故之间的关系。但是，传统方法会分别估计每个严重性级别的崩溃频率，而忽略严重性级别的相关性，这可能导致模型估计有偏差。这项研究的主要目的是开发一个多元Poisson对数正态回归模型（MVPLN），该模型可以同时根据严重性对事故计数进行建模，以评估影响事故频率的因素的影响，并确定不同严重性水平之间的相关性。在美国佛罗里达州的高速公路上的263个出口坡道上收集了崩溃数据。利用马尔可夫链蒙特卡罗（MCMC）方法获得MVPLN模型的估计解。在不同严重性级别的碰撞计数之间发现了显着的相关性。考虑相关结构，在MVPLN模型中探索了每个严重程度级别的碰撞相关因素的影响。

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