Using Data Mining to Analyze the Traffic Data of Intersection

Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The historical traffic data is in fact capable of proving abundant of information that can aid in the development of improved current traffic control. However, many bad observations are hidden in databases with various faces. Data mining tools such as the k-means clustering approach are the keys to explore the information in the traffic data. This paper uses the k-means clustering approach to identify time-of-day (TOD) break points based on the historical data to support the design of signal timing plan. A case study using an intersection corridor was conducted to demonstrate the method.

• Record URL:
  https://doi.org/10.1061/41039(345)526
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784410394
• Supplemental Notes:
  • © 2009 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Yang, Jun
  • Cheng, Wei
  • Li, Xuemin
  • Yuan, Manrong
• Conference:
  • Second International Conference on Transportation Engineering
  • Location: Chengdu , China
  • Date: 2009-7-25 to 2009-7-27
• Publication Date: 2009-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 3195-3200
• Monograph Title: International Conference on Transportation Engineering 2009

Subject/Index Terms

• TRT Terms: Case studies; Cluster analysis; Data mining; Periods of the day; Signalized intersections; Traffic data; Traffic signal timing
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01534554
• Record Type: Publication
• ISBN: 9780784410394
• Files: TRIS, ASCE
• Created Date: Aug 14 2014 9:19AM