4.7 Article

Accident prediction models for urban roads

期刊

ACCIDENT ANALYSIS AND PREVENTION
卷 35, 期 2, 页码 273-285

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0001-4575(02)00005-2

关键词

accident prediction models; generalised linear modelling; road accidents; junction; road links

向作者/读者索取更多资源

This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for strwkninger. Notat 59, The Danish Road Directorate, 1998] (Greibe and Hemdorff, 1995, 1998). The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to 'black spot' identification and network safety analysis undertaken by local road authorities. The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables. The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation ('percentage-explained' value)while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions. Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow. (C) 2002 Elsevier Science 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