SHORT-TERM TRAVEL TIME PREDICTION

This paper proposes a method to predict freeway travel times using a linear model in which the coefficients vary as smooth functions of the departure time. The method uses widely available freeway sensor data and is computationally efficient. To demonstrate the effectiveness of the method, the approach is applied to two field loop detector data sets. The first data set is relatively small in scale, but very high in quality, containing information from probe vehicles and double loop detectors. On this data set, the prediction error ranges from 5% for a trip leaving immediately to 10% for a trip leaving 30 min or more in the future. The method is then applied to a data set on a much larger spatial scale. On this data set, errors range from about 8% at zero lag to 13% at a time lag of 30 min or more. Slightly different versions of the method are compared to enhance understanding of the inner workings of the model. Findings indicate that the quality of the training data used to estimate the model coefficients has significant impact on the prediction accuracy. The potential of a multi-segment version of the model is also demonstrated.

• Record URL:
  http://www.sciencedirect.com/science/article/B6VGJ-495052V-1/2/09b9e55444a7185a944cf0daea5fa53c
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Zhang, X
  • Rice, J A
• Publication Date: 2003-6

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 187-210
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 11
  • Issue Number: 3
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Accuracy; Departure time; Errors; Field studies; Freeways; Linear regression analysis; Loop detectors; Mathematical prediction; Time lag; Travel time
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00961890
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Sep 1 2003 12:00AM