4.7 Article

Safety performance functions using traffic conflicts

期刊

SAFETY SCIENCE
卷 51, 期 1, 页码 160-164

出版社

ELSEVIER
DOI: 10.1016/j.ssci.2012.04.015

关键词

Maximum likelihood estimation; Negative binomial regression; Poisson-gamma hierarchy; Traffic conflicts; Two-phase nested models

向作者/读者索取更多资源

Recent research has shown that traffic conflicts provide useful insight into the failure mechanism that leads to road collisions while being more frequent and of minor social cost. However, the relationship between collisions and conflicts must first be established in order to use traffic conflicts as surrogates to collisions for safety analysis. To investigate the relationship between conflicts and collisions, a two-phase model is proposed where a lognormal model is employed in the first phase to predict conflicts using traffic volume, area type (urban/suburban) and some geometric-related variables as covariates. In the second phase, a conflicts-based negative binomial (NB) safety performance function (SPF) is then employed to predict collisions. The proposed model was applied to a dataset corresponding to 51 signalized intersections in British Columbia. The results show that a significant proportional relationship exists between conflicts and collisions where the moderating effects of conflicts on collisions are non-linear with decreasing rates. The scaled deviance and Pearson chi(2) goodness of fit measures indicated that the proposed NB model has adequately fitted the data. The finding that conflicts can be used to represent collisions calls for further research on the countermeasures needed to reduce conflicts as effective means for decreasing collision frequency. Apart from the traffic- and geometric-based traditional countermeasures, new driving-behavior-based measures should be devised that would hopefully have a downward influence on collisions. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据