The effect of varying parameters and focusing on bus travel time prediction

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

João M. Moreira (Autor) FEUP; Carlos Soares (Autor) FEP; Alípio M. Jorge (Autor) FCUP; Jorge Freire de Sousa (Autor) FEUP

Ata de Conferência Internacional

Título: Advances in Knowledge Discovery and Data Mining

Páginas: 689-696

13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009)

Bangkok, Thailand, April 27-30, 2009

Indexação

ISI Web of Knowledge - 4 Citações

ISI Web of Science

Scopus - 3 Citações

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Tecnológicas > Tecnologia > Tecnologia dos transportes > Tecnologia do transporte rodoviário ; Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-003-NXQ

DOI: 10.1007/978-3-642-01307-2_69

Abstract (EN): Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. ID 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 tusks (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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8