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

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&...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2015-02, Vol.150, p.428-439
Main Authors: Mendes-Moreira, João, Jorge, Alípio Mário, Freire de Sousa, Jorge, Soares, Carlos
Format: Article
Language:English
Subjects:
Algorithms
Dynamic selection
Dynamics
Ensemble learning
Long term
Projection
Pruning
Regression
Robustness
State of the art
Support vector machines
Travel time prediction
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.08.072