基于Adaptive Lasso及RF算法的冰雪天气交通事故分析

doi:10.16265/j.cnki.issn1003-3033.2017.02.018

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (2): 98-103.doi: 10.16265/j.cnki.issn1003-3033.2017.02.018

基于Adaptive Lasso及RF算法的冰雪天气交通事故分析

赵玮^1,2,3 讲师, 徐良杰¹ 教授, 冉斌^3,4 教授, 汪济洲¹, 张璇³

1 武汉理工大学交通学院,湖北武汉 430063
2 内蒙古科技大学经管学院,内蒙古包头 014010
3 威斯康星州立大学工程学院,威斯康辛州麦迪逊 53705,美国
4 东南大学交通学院,江苏南京 210096

收稿日期:2016-11-01 修回日期:2017-01-11 出版日期:2017-02-28 发布日期:2020-11-22
作者简介:赵玮 (1988— ),男,内蒙古包头人,讲师,博士研究生,主要研究方向为交通安全与交通运输规划与管理。E-mail:zw2253782@hotmail.com。
基金资助:
教育部社会科学青年基金资助(16YJCZH157);内蒙古科技大学创新基金资助(2015QDL27);国家重点基础研究发展(“973”)计划项目(2012CB725405)。

Analyzing traffic crash under iced and snow weather condition based on Adaptive Lasso and RF

ZHAO Wei^1,2,3, XU Liangjie¹, RAN Bin^3,4, WANG Jizhou¹, ZHANG Xuan³

1 School of Transportation, Wuhan University of Technology, Wuhan Hubei 430063, China
2 School of Economics and Management, Inner Mongolia University of Science & Technology, Baotou Inner Mongolia 014010, China
3 Civil Engineering, University of Wisconsin, Madison 53705, America
4 School of Transportation, Southeast University, Jiangsu Nanjing 210096, China

Received:2016-11-01 Revised:2017-01-11 Online:2017-02-28 Published:2020-11-22

摘要/Abstract

摘要： 为分析冰雪天气下高速公路交通事故频发致因,量化分析驾驶环境、驾驶员及车辆情况对事故的影响,根据Adaptive Lasso和随机森林(RF)混合算法建立预测模型。以10年约30万组冰雪环境下高速公路交通事故数据为例,训练改进预测模型验证其准确性。结果表明,混合算法的准确度和拟合程度都优于支持向量机(SVM)、分类回归树(CART)及RF等单独算法。交通事故与环境因素相关性最显著,坡路、弯道及交叉口处事故受冰雪环境影响较大;事故与驾驶员因素中部分因素显著相关,如驾驶员性别及安全带使用情况;本地驾驶员对驾驶能力及冰雪环境的估计错误更易导致交通事故。

关键词: 高速公路, 交通事故, Adaptive Lasso, 随机森林(RF), 冰雪天气, 大数据分析

Abstract: In order to analyze the factors affecting traffic crashes under ice and snow weather conditions and make clear of the specific impact factors by driving environment, driver and vehicle conditions. This study analyzes risk factors which are differently relative to crash severity based on hybrid algorithm of Adaptive Lasso and RF which was trained with ten-year three hundred thousand crash data. The results show that the hybrid algorithm is superior to the support vector machine(SVM), the classification and regression trees(CART) and RF in both the accuracy and the fitting degree,that there is the most significant correlation between environmental factors and crashes,that accidents on slopes,curved roads,and at intersections are caused by the decrease in the road surface skid resistance,that there is a negative correlation between fatal crashes and use of safety belts,that woman is liable to make mistakes in driving,and that local drivers usually overestimate their driving skills, which leading to crashes.

Key words: highway, traffic accident, Adaptive Lasso, random forest(RF), iced and snow, big data analysis

中图分类号:

赵玮, 徐良杰, 冉斌, 汪济洲, 张璇. 基于Adaptive Lasso及RF算法的冰雪天气交通事故分析[J]. 中国安全科学学报, 2017, 27(2): 98-103.

ZHAO Wei , XU Liangjie , RAN Bin , WANG Jizhou , ZHANG Xuan. Analyzing traffic crash under iced and snow weather condition based on Adaptive Lasso and RF[J]. China Safety Science Journal, 2017, 27(2): 98-103.

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基于Adaptive Lasso及RF算法的冰雪天气交通事故分析

Analyzing traffic crash under iced and snow weather condition based on Adaptive Lasso and RF

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