Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks

A model for predicting travel times by mining spatio-temporal data acquired from vehicles equipped with Global Positioning System (GPS) receivers in urban traffic networks is presented. The proposed model, which uses k-nearest neighbor (kNN) non-parametric regression, is compared with models that use historical averages and the seasonal autoregressive integrated moving average (ARIMA) model. The main contribution is provision of a methodology for mining GPS data that involves examining areas that cannot be covered with conventional fixed sensors. The work confirms that the method that predicts traffic conditions most accurately on roads and highways (namely seasonal ARIMA) is not optimal for travel time prediction in the context of GPS data from urban travel networks. In all the examined cases, kNN approach yields a mean absolute percentage error that is twice as good as ARIMA, while in some cases it even yields a mean absolute percentage error that is an order of magnitude better. The merit of the model is demonstrated using GPS data collected by vehicles traveling through the road network of the city of Zagreb. To evaluate the performance, the models mean absolute percentage error, mean error, and root mean square error are calculated. A non-parametric ranked Friedman ANOVA to test groups of 3 or more models, and the Wilcoxon matched pairs test to test significance between 2 models are used. The alpha levels are adjusted using the Bonferroni correction. Today’s commercial fastest-route guidance systems can readily incorporate the proposed model. Since the model yields travel times that are dependent on dynamic factors, these commercial systems can be made dynamic. Furthermore, the model can also be used to generate pre-trip information that will help users to save time.

  • Availability:
  • Authors:
    • Markovic, Hrvoje
    • Basic, Bojana Dalbelo
    • Gold, Hrvoje
    • Dong, Fang
    • Hirota, Kaoru
  • Publication Date: 2010

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 1-13
  • Serial:
  • Publication flags:

    Open Access (libre)

Subject/Index Terms

Filing Info

  • Accession Number: 01154980
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 20 2010 12:59PM