Research of Short-Term Traffic Flow Forecast Method Based on the Kalman Filter

The forecasting of traffic flow is an important thesis of the research of intelligent transportation systems (ITS). A good method of traffic flow forecasting can play a very important role in traffic control and transportation programming. This paper first analyzed the Kalman filter theory in detail considering the following features of the Kalman filter method: choose the forecast factors flexibly, forecast accurately, and calculate expediently. Then a short-term traffic volume forecast model based on the Kalman filter theory was established. At last, the paper simulated one segment of the Jing-Jin-Tang freeway using the traffic flow data from a historical monitoring database in order to verify the availability of the advanced model. This research may provide a basis for traffic data analysis and a support for traffic management and traffic control.

• Record URL:
  https://doi.org/10.1061/41186(421)95
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411865
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Chen, Feng
  • Jia, Yuanhua
  • An, Wenjuan
  • Zhang, Na
  • Niu, Zhonghai
• Conference:
  • 11th International Conference of Chinese Transportation Professionals (ICCTP)
  • Location: Nanjing , China
  • Date: 2011-8-14 to 2011-8-17
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 960-968
• Monograph Title: ICCTP 2011: Towards Sustainable Transportation Systems

Subject/Index Terms

• TRT Terms: Forecasting; Intelligent transportation systems; Kalman filtering; Traffic data; Traffic flow; Traffic volume
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning; I73: Traffic Control;

Filing Info

• Accession Number: 01450827
• Record Type: Publication
• ISBN: 9780784411865
• Files: TRIS, ASCE
• Created Date: Oct 31 2012 5:04PM