Exploring the Relationship Between Data Aggregation and Predictability to Provide Better Predictive Traffic Information

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.

• Record URL:
  • https://doi.org/10.3141/1935-04
• 
• Summary URL:
  • http://scholar.google.com/scholar_lookup?title=Exploring+the+Relationship+Between+Data+Aggregation+and+Predictability+to+Provide+Better+Predictive+Traffic+Information&author=C.+Oh&author=S.+Ritchie&author=J.+Oh&publication_year=2005
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0309094097
• Authors:
  • Oh, Cheol
  • Ritchie, Stephen G
  • Oh, Jun-Seok
• Publication Date: 2005

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 28-36
• Monograph Title: Information Systems and Technology
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 1935
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Accuracy; Kalman filtering; Mathematical prediction; Neural networks; Test beds; Travel time
• Candidate Terms: Data aggregation
• Uncontrolled Terms: Adaptive exponential smoothing; Autoregressive models
• Geographic Terms: Irvine (California)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01023231
• Record Type: Publication
• ISBN: 0309094097
• Files: TRIS, TRB, ATRI
• Created Date: Apr 25 2006 5:03PM