Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

被引:104
作者
Chan, Chun Yip [2 ]
Huang, Baoshan [2 ]
Yan, Xuedong [1 ]
Richards, Stephen [2 ]
机构
[1] Beijing Jiaotong Univ, MOE Key Lab Transportat Complex Syst Theory & Tec, Sch Traff & Transportat, Beijing 100044, Peoples R China
[2] Univ Tennessee, Dept Civil & Environm Engn, Knoxville, TN USA
基金
中央高校基本科研业务费专项资金资助;
关键词
pavement condition; highway safety; Negative Binomial Regression; rut depth International Roughness Index; Present Serviceability Index; ROUGHNESS; MODELS;
D O I
10.1002/atr.129
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness-of-fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright (C) 2010 John Wiley & Sons, Ltd.
引用
收藏
页码:150 / 161
页数:12
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