...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Accident Analysis & Prevention >Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data
【24h】

Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data

机译：使用分层贝叶斯二进位概率模型通过实时交通数据分析高速设施上的碰撞伤害严重性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities' (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers' characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (1-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models' results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed.
机译：严重的撞车事故正在造成严重的社会和经济损失，因此，降低撞车事故的严重程度已成为高速设施（高速公路和高速公路）管理的主要目标之一。传统的碰撞伤害严重性分析主要利用来自碰撞报告的数据，这些报告涉及碰撞发生信息，驾驶员特征以及与道路几何相关的变量。在这项研究中，引入了实时交通和天气数据以分析碰撞伤害的严重程度。通过自动车辆识别（AVI）系统在两条道路上捕获的空间平均速度在本研究中用作解释变量。来自山区高速公路（科罗拉多州1-70）和城市高速公路（奥兰多州408号州际公路）的数据已用于识别分析结果的一致性。估计使用二进制概率（BP）模型对非严重（仅财产损失）崩溃和严重（伤害和死亡）崩溃进行分类。首先，将贝叶斯BP模型的结果与最大似然估计BP模型的结果进行比较，得出的结论是，贝叶斯推断在具有更大的显着变量的情况下是优越的。然后，开发了不同级别的分层贝叶斯BP模型，这些模型具有随机效应，分别解释了段级别和崩溃单个级别的未观察到的异质性。来自两个研究位置的模型结果表明，在发生碰撞之前速度的较大变化会增加发生严重碰撞的可能性。此外，考虑到贝叶斯BP模型中未观察到的异质性，模型拟合优度已得到大幅改善。最后，讨论了模型结果和分层贝叶斯概率模型的未来可能应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号