摘要
通过调查问卷收集2391名出租车驾驶员个体属性、身体疲劳感知、工作压力、风险驾驶行为和交通事故经历的相关信息。运用多指标多原因(MIMIC)模型进行路径分析,探究身体疲劳感知、风险驾驶行为对交通事故的诱发效应,验证性别、年龄、工作压力的原因变量对身体疲劳感知、分心驾驶行为的影响作用。选取逻辑回归、朴素贝叶斯、支持向量机、随机森林4种机器学习算法对出租车事故进行预测。结果表明:身体疲劳感知、过失性驾驶行为、激进性驾驶行为与分心驾驶行为的提高能够导致事故率的提高,性别、年龄、工作压力会对身体疲劳感知与分心驾驶行为的频次产生影响。基于机器学习的事故预测模型效果极佳,其中随机森林的预测效果最好,使用单一特征变量“风险驾驶行为”、“分心驾驶行为”、“身体疲劳感知”的预测精度尚可接受。当引入“工作压力”、“年龄”、“性别”的个人属性指标时,预测精度进一步提高。
Through the questionnaire, 2391 taxi drivers’ personal attributes, physical fatigue perception, work load, risk driving behaviors and road traffic accident experience were collected. Multiple indicators and multiple causes(MIMIC) model is used to explore the induced effects of physical fatigue perception, risky driving behavior on traffic accidents, and to verify the effects of causal variable for gender, age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms, including logistic regression, naive Bayes, support vector machine and random forest, are selected to predict the taxi accident. The results show that: the increase of physical fatigue perception, errors driving behavior, radical driving behavior and distracted driving behavior can lead to the growth of accident rate, and gender, age and work load can affect the physical fatigue perception and frequency of distracted driving behavior. The accident prediction model based on machine learning is very effective, and the prediction effect of random forest is the highest, and the prediction accuracy of single characteristic variables ’risky driving behavior’, ’distracted driving behavior’ and ’physical fatigue perception’ is acceptable.When the personal attribute indicators of ’working load’, ’age’, and ’gender’ are introduced, the prediction accuracy can be further improved.
作者
潘恒彦
张文会
梁婷婷
彭志鹏
高维
王永岗
PAN Heng-yan;ZHANG Wen-hui;LIANG Ting-ting;PENG Zhi-peng;GAO Wei;WANG Yong-gang(College of Transportation Engineering,Chang′an University,Xi′an 710064,China;School of Traffic and Transportation,Northeast Forestry University,Harbin 150040,China;School of Civil Engineering,Xi′an Traffic Engineering Institute,Xi′an 710399,China;School of Transportation Science and Engineering,Harbin Institute of Technology,Harbin 150090,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第2期457-467,共11页
Journal of Jilin University:Engineering and Technology Edition
基金
国家社会科学基金项目(19BGL239)。
关键词
交通运输安全工程
出租车驾驶员
道路交通事故
MIMIC模型
路径分析
中介效应
机器学习
engineering of communications and transportation safety
professional taxi driver
road traffic accidents
MIMIC model
path analysis
moderation effect
machine learning
