Analysis of freeway accident frequency using multivariate adaptive regression splines

被引：14
|
作者：
Chang, Li-yen [1 ]
Chu, Hsing-chung [1 ]
Lin, Da-jie [2 ]
Lui, Pei [2 ]
机构：
[1] Natl Chia Yi Univ, Grad Inst Mkt & Logist Transportat, 580 Sin Min Rd, Chiayi 60054, Taiwan
[2] Feng Chia Univ, Dept Transportat Technol & Management, Taichung 40724, Taiwan
来源：
2012 INTERNATIONAL SYMPOSIUM ON SAFETY SCIENCE AND TECHNOLOGY | 2012年 / 45卷
关键词：
freeway; accident frequency; linear regression; data mining; multivariate adaptive regression splines (MARS);
D O I：
10.1016/j.proeng.2012.08.245
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This study applies nonparametric multivariate adaptive regression splines (MARS) modeling technique to explore the effects of nonbehavior factors including highway geometrics characteristics, traffic factors as well as environmental conditions on freeway accidents. 2007-2008 accident data of National Freeway 1 in Taiwan were collected for analysis. The results indicate that horizontal alignment, vertical alignment, average daily traffic volume (ADT), heavy vehicle ADT and annual precipitation have are nonlinear effects on freeway accidents. (C) 2012 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：824 / 829
页数：6
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