交通运输系统工程与信息

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (6): 82-87.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于车联网数据挖掘的营运车辆 驾驶速度行为聚类研究

孙川a,b，吴超仲a,b，褚端峰*a,b，杜志刚a,b,c，田飞a,b   

  1. 武汉理工大学a.智能交通系统研究中心；b.水路公路交通安全控制与装备教育部工程研究中心；c.交通学院，武汉430063
  • 收稿日期:2015-03-17 修回日期:2015-08-28 出版日期:2015-12-25 发布日期:2015-12-25
  • 作者简介:孙川（1989-），男，湖北十堰人，博士生.
  • 基金资助:

    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).

Driving Speed Behavior Clustering for Commercial Vehicle Based on Connected Vehicle Data Mining

SUN Chuan a,b，WU Chao-zhong a,b，CHU Duan-feng a,b，DU Zhi-gang a,b,c，TIAN Feia   

  1. a. Intelligent Transportation Systems Research Center; b. Engineering Research Center for Transportation Safety, Ministry of Education; c. School of Transportation,Wuhan University of Technology,Wuhan 430063, China
  • Received:2015-03-17 Revised:2015-08-28 Online:2015-12-25 Published:2015-12-25

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被引次数

15

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.

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

Abstract:

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.

Key words: traffic engineering, driving speed behavior, data mining, commercial vehicle, connected vehicle, factor analysis, cluster analysis

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