A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

Pedro Lopez-Garcia; Enrique Onieva; Eneko Osaba; Antonio D. Masegosa; Asier Perallos

doi:10.1109/TITS.2015.2491365

A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

Pedro Lopez-Garcia
, Enrique Onieva
, Eneko Osaba
, Antonio D. Masegosa
, Asier Perallos

University of Deusto

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

141 Citas (Scopus)

Resumen

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.

Idioma original	Inglés
Número de artículo	7331637
Páginas (desde-hasta)	557-569
Número de páginas	13
Publicación	IEEE Transactions on Intelligent Transportation Systems
Volumen	17
N.º	2
DOI	https://doi.org/10.1109/TITS.2015.2491365
Estado	Publicada - feb 2016
Publicado de forma externa	Sí

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ODS 11: Ciudades y comunidades sostenibles

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10.1109/TITS.2015.2491365

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abstract = "This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.",

keywords = "Intelligent transportation systems, cross entropy, curse of dimensionality, fuzzy logic, fuzzy systems, genetic algorithms, hierarchical fuzzy-rule-based systems, traffic congestion prediction",

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AU - Perallos, Asier

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A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

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