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

Research output: Contribution to journal › Article › peer-review

140 Citations (Scopus)

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.

Original language	English
Article number	7331637
Pages (from-to)	557-569
Number of pages	13
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	17
Issue number	2
DOIs	https://doi.org/10.1109/TITS.2015.2491365
Publication status	Published - Feb 2016
Externally published	Yes

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TITS.2015.2491365

Cite this

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

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

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AU - Lopez-Garcia, Pedro

AU - Onieva, Enrique

AU - Osaba, Eneko

AU - Masegosa, Antonio D.

AU - Perallos, Asier

PY - 2016/2

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AB - 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.

KW - cross entropy

KW - curse of dimensionality

KW - fuzzy logic

KW - fuzzy systems

KW - genetic algorithms

KW - hierarchical fuzzy-rule-based systems

KW - Intelligent transportation systems

KW - traffic congestion prediction

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

Abstract

Keywords

UN SDGs

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