The Effect of Varying Parameters and Focusing on Bus Travel Time Prediction

  • Authors:

  • João M. Moreira;Carlos Soares;Alípio M. Jorge;Jorge Freire Sousa

  • Affiliations:

  • Faculdade de Engenharia, Universidade do Porto, DEI, Portugal and LIAAD-INESC Porto L.A., Portugal;Faculdade de Economia, Universidade do Porto, Portugal and LIAAD-INESC Porto L.A., Portugal;Faculdade de Economia, Universidade do Porto, Portugal and LIAAD-INESC Porto L.A., Portugal;Faculdade de Engenharia, Universidade do Porto, DEIG, Portugal

  • Venue:
  • PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
  • Year:
  • 2009

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Abstract

Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. In this paper we investigate the use of regression methods for the problem of predicting the travel time of buses in a Portuguese public transportation company. More specifically, we empirically evaluate the impact of varying parameters on the performance of different regression algorithms, such as support vector machines (SVM), random forests (RF) and projection pursuit regression (PPR). We also evaluate the impact of the focusing tasks (example selection, domain value definition and feature selection) in the accuracy of those algorithms. Concerning the algorithms, we observe that 1) RF is quite robust to the choice of parameters and focusing methods; 2) the choice of parameters for SVM can be made independently of focusing methods while 3) for PPR they should be selected simultaneously. For the focusing methods, we observe that a stronger effect is obtained using example selection, particularly in combination with SVM.