Stream travel time prediction using particle filtering approach

Travel-time information is an integral part of Advanced Traveler Information Systems and Advanced Traffic Management Systems. Real-time estimation of stream travel time will be helpful in making trip decisions such as route choice and departure time. The present study proposes a method to predict stream travel time using particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. The present model hypothesizes the median of historical travel times obtained from the collected data as a priori estimate and hence predicting the actual travel time will be equivalent to forecasting the variations in travel time according to the current measurements. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy, quantified using the Mean Absolute Percentage Error and Mean Absolute Error was observed to be satisfactory. Performance of this method was compared to a basic speed-based approach, where travel time is obtained by time–distance relationship using space-mean speed, and the proposed method showed better performance.

• Record URL:
  https://doi.org/10.1080/19427867.2016.1192016
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19427867
• Supplemental Notes:
  • © 2016 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Dhivyabharathi, B
  • Hima, E S
  • Vanajakshi, L
• Publication Date: 2018-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 75-82
• Serial:
  • Transportation Letters: The International Journal of Transportation Research
  • Volume: 10
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1942-7867
  • EISSN: 1942-7875
  • Serial URL: http://www.tandfonline.com/toc/ytrl20/current

Subject/Index Terms

• TRT Terms: Advanced traveler information systems; Bayes' theorem; Bluetooth technology; Dynamic models; Mathematical models; Mathematical prediction; Travel time
• Uncontrolled Terms: Particle filtering
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01667390
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 26 2018 9:13AM