计算机应用 ›› 2019, Vol. 39 ›› Issue (3): 907-912.DOI: 10.11772/j.issn.1001-9081.2018081699

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于区域道路实况数据的交通行为谱分析方法

黄丰雨1,2, 吴业福1,2, 陈镜任1,2, 吴冰1,2   

  1. 1. 武汉理工大学 计算机科学与技术学院, 武汉 430063;
    2. 交通物联网技术湖北省重点实验室(武汉理工大学), 武汉 430063
  • 收稿日期:2018-08-17 修回日期:2018-10-21 出版日期:2019-03-10 发布日期:2019-03-11
  • 通讯作者: 吴业福
  • 作者简介:黄丰雨(1994-),男,江西抚州人,硕士研究生,主要研究方向:计算机网络、智能交通;吴业福(1964-),男,湖北红安人,副教授,博士,主要研究方向:交通信息与安全、人工智能;陈镜任(1996-),女,江西南昌人,硕士研究生,主要研究方向:机器学习、大数据分析、交通安全;吴冰(1992-),男,湖北武汉人,硕士,主要研究方向:大数据分析、交通安全。

Traffic behavior spectrum analysis method based on regional road live data

HUANG Fengyu1,2, WU Yefu1,2, CHEN Jingren1,2, WU Bing1,2   

  1. 1. College of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430063, China;
    2. Hubei Key Laboratory of Transportation Internet of Things(Wuhan University of Technology), Wuhan Hubei 430063, China
  • Received:2018-08-17 Revised:2018-10-21 Online:2019-03-10 Published:2019-03-11

摘要:

针对国内外有关交通行为谱研究的特征指标和评价指标不完善,且不能定量分析等问题,设计并定义了相应的特征指标和评价指标以建立完整的、能够定量化的分析区域交通行为数据的交通行为谱体系。首先基于交通行为特征,采用改进的层次分析法(AHP)对交通秩序类型进行了分类;其次采用多数据融合的实时系统集成(RTSI)算法对某路段交通安全性进行综合评判。最后开发了交通行为谱分析工具,该工具能根据交通实况数据计算区域路段的交通安全指数,较为完备地分析该路段内的交通行为。

关键词: 交通行为谱, 交通行为谱分析工具, 区域道路实况数据, 交通行为特征, 多数据融合

Abstract:

Aiming at the problem that the characteristic and evaluation indexes for reasearch of traffic behavior spectrum are incompleted both at home and abroad and quantitative analysis cannot be performed in the research, the corresponding characteristic and evaluation indexes were defined to establish a complete traffic behavior spectrum system with quantitative analysis of regional traffic behavior data. Firstly, based on the characteristics of traffic behavior, an improved Analytic Hierarchy Process (AHP) was used to classify the traffic order types. Secondly, Real-Time System Integration (RTSI) algorithm with multi-data fusion was used to comprehensively evaluate the traffic safety of a certain road. Finally, a traffic behavior spectrum analysis tool was developed, calculating traffic safety index of a road section according to the traffic live data, and analyzing traffic behavior in the section more completely.

Key words: traffic behavior spectrum, analysis tool of traffic behavior spectrum, regional road live data, traffic behavior characteristic, multi-data fusion

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