Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study

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

doi:10.1007/978-3-319-44636-3_27

Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study

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

^*Autor correspondiente de este trabajo

University of Deusto

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

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 30min. 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.

Idioma original	Inglés
Título de la publicación alojada	Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings
Editores	Edurne Barrenechea, Alicia Troncoso, José A. Gámez, Oscar Luaces, Héctor Quintián, Emilio Corchado, Mikel Galar
Editorial	Springer Verlag
Páginas	290-299
Número de páginas	10
ISBN (versión impresa)	9783319446356
DOI	https://doi.org/10.1007/978-3-319-44636-3_27
Estado	Publicada - 2016
Publicado de forma externa	Sí
Evento	17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016 - Salamanca, Espana Duración: 14 sept 2016 → 17 sept 2016

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	9868 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016
País/Territorio	Espana
Ciudad	Salamanca
Período	14/09/16 → 17/09/16

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 11: Ciudades y comunidades sostenibles

Acceder al documento

10.1007/978-3-319-44636-3_27

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Lopez-Garcia, P., Osaba, E., Onieva, E., Masegosa, A. D., & Perallos, A. (2016). Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study. En E. Barrenechea, A. Troncoso, J. A. Gámez, O. Luaces, H. Quintián, E. Corchado, & M. Galar (Eds.), Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings (pp. 290-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9868 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-44636-3_27

Lopez-Garcia, Pedro ; Osaba, Eneko ; Onieva, Enrique et al. / Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods : A comparative study. Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings. editor / Edurne Barrenechea ; Alicia Troncoso ; José A. Gámez ; Oscar Luaces ; Héctor Quintián ; Emilio Corchado ; Mikel Galar. Springer Verlag, 2016. pp. 290-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{847f2f4f41ac45558f9419e159128baf,

title = "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 30min. 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.",

keywords = "Classification, Cross entropy, Fuzzy rule-based systems, Genetic algorithms, Hybrid optimization, Intelligent transportation systems, Machine learning",

author = "Pedro Lopez-Garcia and Eneko Osaba and Enrique Onieva and Masegosa, \{Antonio D.\} and Asier Perallos",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016 ; Conference date: 14-09-2016 Through 17-09-2016",

year = "2016",

doi = "10.1007/978-3-319-44636-3\_27",

language = "English",

isbn = "9783319446356",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "290--299",

editor = "Edurne Barrenechea and Alicia Troncoso and G{\'a}mez, \{Jos{\'e} A.\} and Oscar Luaces and H{\'e}ctor Quinti{\'a}n and Emilio Corchado and Mikel Galar",

booktitle = "Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings",

address = "Germany",

}

Lopez-Garcia, P, Osaba, E, Onieva, E, Masegosa, AD & Perallos, A 2016, Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study. En E Barrenechea, A Troncoso, JA Gámez, O Luaces, H Quintián, E Corchado & M Galar (eds.), Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9868 LNAI, Springer Verlag, pp. 290-299, 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Espana, 14/09/16. https://doi.org/10.1007/978-3-319-44636-3_27

Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study. / Lopez-Garcia, Pedro; Osaba, Eneko; Onieva, Enrique et al.
Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings. ed. / Edurne Barrenechea; Alicia Troncoso; José A. Gámez; Oscar Luaces; Héctor Quintián; Emilio Corchado; Mikel Galar. Springer Verlag, 2016. p. 290-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9868 LNAI).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods

T2 - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016

AU - Lopez-Garcia, Pedro

AU - Osaba, Eneko

AU - Onieva, Enrique

AU - Masegosa, Antonio D.

AU - Perallos, Asier

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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 30min. 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.

AB - 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 30min. 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.

KW - Classification

KW - Cross entropy

KW - Fuzzy rule-based systems

KW - Genetic algorithms

KW - Hybrid optimization

KW - Intelligent transportation systems

KW - Machine learning

UR - https://www.scopus.com/pages/publications/84988575903

U2 - 10.1007/978-3-319-44636-3_27

DO - 10.1007/978-3-319-44636-3_27

M3 - Conference contribution

AN - SCOPUS:84988575903

SN - 9783319446356

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 290

EP - 299

BT - Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings

A2 - Barrenechea, Edurne

A2 - Troncoso, Alicia

A2 - Gámez, José A.

A2 - Luaces, Oscar

A2 - Quintián, Héctor

A2 - Corchado, Emilio

A2 - Galar, Mikel

PB - Springer Verlag

Y2 - 14 September 2016 through 17 September 2016

ER -

Lopez-Garcia P, Osaba E, Onieva E, Masegosa AD, Perallos A. Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study. En Barrenechea E, Troncoso A, Gámez JA, Luaces O, Quintián H, Corchado E, Galar M, editores, Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings. Springer Verlag. 2016. p. 290-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-44636-3_27

Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study

Resumen

Serie de la publicación

Conferencia

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto