Improving the accuracy of long-term travel time prediction using heterogeneous ensembles

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Joao Mendes Moreira (Autor) FEUP; Alipio Mario Jorge (Autor) FCUP; Jorge Freire de Sousa (Autor) FEUP; Carlos Soares (Autor) FEUP

Revista

Título: Neurocomputing

Vol. 150 Nº Part B

Páginas: 428-439

ISSN: 0925-2312

Editora: Elsevier

Indexação

ISI Web of Knowledge - 31 Citações

Scopus - 33 Citações

Classificação Científica

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

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-00A-3NB

DOI: 10.1016/j.neucom.2014.08.072

Abstract (EN): This paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12