Dynamic Freeway Corridor Travel Time Prediction Using Single Inductive Loop Detector Data

This study develops a dynamic short-term corridor travel time prediction model using the traffic flow data collected by single loop detectors. This method involves a multi-step-ahead prediction of traffic condition with a seasonal autoregressive integrated moving average model. In order to adjust the prediction error based on traffic flow data that becomes available in real time, an embedded adaptive Kalman filter is designed. Then, an on-line corridor travel time prediction model is developed. Test results show that this method is able to capture the traffic dynamics and provide more accurate travel time prediction. The proposed method is promising in real-time corridor travel time prediction for various traffic applications under the context of Intelligent Transportation Systems.

• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Xia, Jingxin
  • Chen, Mei
• Conference:
  • Transportation Research Board 88th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2009-1-11 to 2009-1-15
• Date: 2009

Language

• English

Media Info

• Media Type: DVD
• Features: Figures; Tables;
• Pagination: 16p
• Monograph Title: TRB 88th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

• TRT Terms: Estimating; Kalman filtering; Loop detectors; Traffic flow; Traffic forecasting; Traffic models; Traffic volume; Travel time
• Subject Areas: Data and Information Technology; Highways; I71: Traffic Theory;

Filing Info

• Accession Number: 01137494
• Record Type: Publication
• Report/Paper Numbers: 09-3022
• Files: BTRIS, TRIS, TRB
• Created Date: Jul 31 2009 8:47AM