Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

• Record URL:
  https://doi.org/10.3141/2554-07
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309441247
• Authors:
  • Allström, Andreas
  • Ekström, Joakim
  • Gundlegård, David
  • Ringdahl, Rasmus
  • Rydergren, Clas
  • Bayen, Alexandre M
  • Patire, Anthony D
• Publication Date: 2016

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 60–68
• Monograph Title: Freeway Operations; Regional Systems Management and Operations; Managed Lanes
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2554
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Kalman filtering; Mathematical prediction; Neural networks; Nonparametric analysis; Parametric analysis; Traffic flow; Travel time; Urban areas
• Uncontrolled Terms: Hybrid methods; Traffic state estimation
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01590254
• Record Type: Publication
• ISBN: 9780309441247
• Report/Paper Numbers: 16-0457
• Files: TRIS, TRB, ATRI
• Created Date: Feb 16 2016 2:35PM