Technical reports

Road traffic analysis using MIDAS data: journey time prediction

R.J. Gibbens, Y. Saacti

December 2006, 35 pages

Department for Transport Horizons Research Programme “Investigating the handling of large transport related datasets” (project number H05-217)

DOI: 10.48456/tr-676

Abstract

The project described in this report was undertaken within the Department for Transport’s second call for proposals in the Horizons research programme under the theme of “Investigating the handling of large transport related datasets”. The project looked at the variability of journey times across days in three day categories: Mondays, midweek days and Fridays. Two estimators using real-time data were considered: a simple-to-implement regression-based method and a more computationally demanding k-nearest neighbour method. Our example scenario of UK data was taken from the M25 London orbital motorway during 2003 and the results compared in terms of the root-mean-square prediction error. It was found that where the variability was greatest (typically during the rush hours periods or periods of flow breakdowns) the regression and nearest neighbour estimators reduced the prediction error substantially compared with a naive estimator constructed from the historical mean journey time. Only as the lag between the decision time and the journey start time increased to beyond around 2 hours did the potential to improve upon the historical mean estimator diminish. Thus, there is considerable scope for prediction methods combined with access to real-time data to improve the accuracy in journey time estimates. In so doing, they reduce the uncertainty in estimating the generalized cost of travel. The regression-based prediction estimator has a particularly low computational overhead, in contrast to the nearest neighbour estimator, which makes it entirely suitable for an online implementation. Finally, the project demonstrates both the value of preserving historical archives of transport related datasets as well as provision of access to real-time measurements.

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BibTeX record

@TechReport{UCAM-CL-TR-676,
  author =	 {Gibbens, R.J. and Saacti, Y.},
  title = 	 {{Road traffic analysis using MIDAS data: journey time
         	   prediction}},
  year = 	 2006,
  month = 	 dec,
  url = 	 {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-676.pdf},
  institution =  {University of Cambridge, Computer Laboratory},
  doi = 	 {10.48456/tr-676},
  number = 	 {UCAM-CL-TR-676}
}