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Collective Traffic Forecasting

  • Marco Lippi
  • Matteo Bertini
  • Paolo Frasconi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6322)

Abstract

Traffic forecasting has recently become a crucial task in the area of intelligent transportation systems, and in particular in the development of traffic management and control. We focus on the simultaneous prediction of the congestion state at multiple lead times and at multiple nodes of a transport network, given historical and recent information. This is a highly relational task along the spatial and the temporal dimensions and we advocate the application of statistical relational learning techniques. We formulate the task in the supervised learning from interpretations setting and use Markov logic networks with grounding-specific weights to perform collective classification. Experimental results on data obtained from the California Freeway Performance Measurement System (PeMS) show the advantages of the proposed solution, with respect to propositional classifiers. In particular, we obtained significant performance improvement at larger time leads.

Keywords

Intelligent Transportation System Inductive Logic Programming Time Series Forecast Prediction Horizon Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marco Lippi
    • 1
  • Matteo Bertini
    • 1
  • Paolo Frasconi
    • 1
  1. 1.Dipartimento Sistemi e InformaticaUniversità degli Studi di Firenze 

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