Travel Time Prediction with Rough Set Models

More and more license plate cameras have been employed in cities such as. Beijing (China), Stockholm (Sweden), Delft (Netherlands), etc. Those license plate cameras can capture time instances when vehicles pass by the cameras, and then derive individual travel time measurements. Those measurements might be supplementary for fixed position detectors (FPD) (e.g. inductive loop detectors). The main objective of this paper is to utilize two different data sources (inductive loop detectors and license plate cameras) for travel time prediction. This paper presents a rough set model to address the problem of travel time prediction. Since people draw conclusions of traffic conditions in a vague way. Predicting travel time is not necessary with high resolution, while a rough classification might be enough. A densely used urban arterial in Netherlands was selected to test the performance of this model. The results of the comparisons indicate that this proposed model is capable of dealing with complex nonlinear urban arterial travel time prediction with satisfying effectiveness, robustness, and reliability.


  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; References; Tables;
  • Pagination: 10p
  • Monograph Title: ITS Connections: Saving Time. Saving Lives

Subject/Index Terms

Filing Info

  • Accession Number: 01145150
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 25 2009 11:00AM