摘要
为了充分利用交通运输企业积累的海量车联网数据,挖掘营运车辆驾驶行为特征的潜在规律.根据车联网数据属性提取涉及驾驶行为特征的参数,基于因子分析把8个驾驶行为特征参数化为少数几个蕴含明确驾驶行为信息的综合变量,以此为指标通过系统聚类,将选取的江苏范围内营运车辆驾驶行为特征进行聚类分析.结果表明,营运车辆驾驶行为特征可有效聚为变速行为、超速行为、减速行为、加速行为,其中变速驾驶行为程度较重的驾驶人其他3种驾驶行为程度也较大.这类驾驶人具有较高驾驶风险,交通运输企业需要对其重点监控.研究结果对我国营运车辆驾驶人的监管与培训具有一定参考作用.
In order to take full advantage of mass connected vehicle data from transportation enterprise and find potential laws of driving behavior characteristic, the parameter of driving behavior characteristic are extracted based on the data. Then eight parameters of driving behavior characteristic transform into several aggregate variables of specific driving behavior information based on factor analysis, and driving behavior for commercial vehicle is analyzed by hierarchical clustering in Jiangsu province. Results show that driving behavior for commercial vehicle can be divided into four classes reasonably, such as speed changing,speeding, deceleration, acceleration. Particularly, drivers of speed changing have other driving behaviors and the levels are high too, and they are high-risk drivers, as an important factor of influence road traffic safety,so transportation enterprises could monitor them specially. Research results are of positive significance to improve the monitoring capability of drivers for commercial vehicle.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2015年第6期82-87,共6页
Journal of Transportation Systems Engineering and Information Technology
基金
国家科技支撑计划项目(2014BAG01B03)
国家自然科学基金项目(51105286)
智能交通系统广西高校重点实验室开放基金项目(K201501)
车路协同与安全控制北京市重点实验室开放基金(KFJJ-201401)
同济大学道路与交通工程教育部重点实验室开放基金(K201301)
关键词
交通工程
驾驶速度行为
数据挖掘
营运车辆
车联网
因子分析
聚类分析
traffic engineering
driving speed behavior
data mining
commercial vehicle
connected vehicle
factor analysis
cluster analysis
