Short term traffic congestion forecasting using hybrid metaheuristics and rule based methods a comparative study

Abstract

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.

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• https://zenodo.org/record/164849 under the license https://creativecommons.org/licenses/by-nc-nd

• https://zenodo.org/record/164849/files/MAEB.pdf under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://link.springer.com/content/pdf/10.1007/978-3-319-44636-3_27,

http://dx.doi.org/10.1007/978-3-319-44636-3_27 under the license cc-by-nc-nd

• https://link.springer.com/chapter/10.1007/978-3-319-44636-3_27,

https://www.scipedia.com/public/Lopez-Garcia_et_al_2016b,

https://dx.doi.org/10.1007/978-3-319-44636-3_27,

http://dx.doi.org/10.1007/978-3-319-44636-3_27,

https://dblp.uni-trier.de/db/conf/caepia/caepia2016.html#Lopez-GarciaOOM16,

https://rd.springer.com/chapter/10.1007/978-3-319-44636-3_27,

https://core.ac.uk/display/144755094,

https://academic.microsoft.com/#/detail/2514081068 under the license http://www.springer.com/tdm