期刊文献+

基于手机定位的高速公路事件检测方法研究 被引量:5

Expressway Automatic Incident Detection Method Using Mobile Phone Positioning
下载PDF
导出
摘要 改善自动事件检测方法的检测率(DR)、误报率(FAR)和平均检测时间(MTTD),对于提高事件管理效率至关重要。在分析现有检测方法的基础上,提出基于手机定位的高速公路事件检测方法并给出一种检测算法。算法以在路车辆上每部手机的定位数据为基础,由此获取平均速度、速度标准差和速度变化系数作为特性参数,采用模糊综合评判方法判断道路交通运行状态,实现高速公路事件的自动检测。模拟实验证明了该算法的有效性。 It is critical that Detection Rate (DR), False Alarm Rate (FAR) and Mean Time to Detection (MTrD) are improved in expressway automatic incident detection. Based on the analysis of expressway automatic incident detection methods, an expressway incident detection method using mobile phone positioning is presented. The average speed, standard deviation of speed, variation coeffticient of speed of all mobile phones on vehicles on certain expressway area are defined as key parameters, which are used automatically determine expressway incidents with fuzzy comprehensive evaluation.Simulation results show that DR, FAR and MTTD of the proposed method are improved.
出处 《公路交通科技》 CAS CSCD 北大核心 2006年第2期133-136,共4页 Journal of Highway and Transportation Research and Development
基金 国家十五科技攻关计划资助项目(2002BA404A07) 重庆市自然科学基金资助项目(8599)
关键词 事件检测 手机定位 高速公路 模糊综合评判 Incident detection Mobile phone positioning Expressway Fuzzy comprehensive evaluation
  • 相关文献

参考文献8

  • 1Kaan Ozbay,Pushkin Kachroo.Incident Management in Intelligent Transportation Systems[M].Boston London:Artech House,1999.
  • 2Peter Martin,Joseph Perrin,Blake Hansen.Incident Detection Algorithm Evaluation[R].http://www.ndsu.nodak.eduh/n dsu/ugpti/MPC-Pubs/html/MPC01-122.html,March,2001.
  • 3Shu-Ching Chen.Learning-Based Spatio-Temporal Vehicle Tracking and Indexing for Transportation Multimedia Database Systems[J].IEEE Transaction on Intelligent Transportation Systems,2003,4 (3):154-167.
  • 4杨耀华,李昕,江芳泽.高速公路事件探测系统及算法[J].公路交通科技,2003,20(3):133-136. 被引量:8
  • 5Yilin Zhao.Mobile Phone Location Determination and Its Impact on Intelligent Transportation System[J].IEEE Transaction on Intelligent Transportations,2000,1 (1):55-64.
  • 6Brian L Smith,Han Zhang.Mike Fontaine Matt Green.Final report of ITS Center project:Cellphone probes as an ATMS tool[R].http://cts.Virginia.Edu,2003.
  • 7M William Sermons,Frank S Koppelman.Use of Vehicle Positioning Data for Arterial Incident Detection[J].Transpn Res.-C,1996,4(2):87-96.
  • 8Ygnace J-L,Drane C.Cellular Telecommunication and Transportation Convergence:Case Study of a Research Conducted in California and in France on Cellular Positioning Techniques and Transportation Issues[R].Proc:4th International IEEE Conference on Intelligent Transportation Systems,2001.

二级参考文献8

  • 1Payne H J, Knobel. Development and Testing of Incident Detection Algorithms [R]. User Guidelines. Federal Highways Report FHWA-RD-76-21. 1978, 3.
  • 2Persaud B N, Hall L M Catastrophe Theory and Patterns in 30-Second Freeway Traffic Data--Implications for Incident Detection[ J ].Transp. Res., 1990, 23A (2): 103-113.
  • 3Chang E C Wang S H. Improved Freeway Incident Detection Using Fuzzy Set Theory[C]. Washington, Transp. Res. Rec. 1453, Transportation Research Board, 1994: 75-82.
  • 4Lin C K, Chang G L. Development of A Fuzzy-expert System for Incident Detection and Classification[ J ]. Math. And Comp. Modelling,27 (9-11), 1998, 9-25.
  • 5Cheu R L, Ritchie S G. Automated Detection of lane-blocking Freeway Incidents Using Aaificial Neural Networks [ J ]. Transp. Res. 1995,3C (6): 371-388.
  • 6Hsiao C H, Lin C T, Cassidy M. Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway traffic Incidents [ J ]. J.Transp. Engrg., ASCE, 1994, 120 (5): 753-772.
  • 7Geng J, Lee T N. Freeway Traffic Incident Detection Using Fuzzy CMAC Neural Networks[J]. Piscataway, N. J: Proc. IEEE Int. Conf. on Fuzzy Sys., 1998: 1164- 1169.
  • 8Adeli H, Karim A. Fuzzy-wavelet RBFNN Model for Freeway Incident Detection [J]. J. Transp. Engrg., ASCE, 2000, 11/12: 464-471.

共引文献7

同被引文献61

引证文献5

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部