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

• Type: Article in International Scientific Journal
• Date: 2015

Authors

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

Journal

Title: Neurocomputing

Vol. 150 No. Part B

Pages: 428-439

ISSN: 0925-2312

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 31 Citations

Scopus - 33 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12