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

^*Corresponding author for this work

University of Deusto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Proceedings
Editors	Edurne Barrenechea, Alicia Troncoso, José A. Gámez, Oscar Luaces, Héctor Quintián, Emilio Corchado, Mikel Galar
Publisher	Springer Verlag
Pages	290-299
Number of pages	10
ISBN (Print)	9783319446356
DOIs	https://doi.org/10.1007/978-3-319-44636-3_27
Publication status	Published - 2016
Externally published	Yes
Event	17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016 - Salamanca, Spain Duration: 14 Sept 2016 → 17 Sept 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9868 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016
Country/Territory	Spain
City	Salamanca
Period	14/09/16 → 17/09/16

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

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

Access to Document

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

Cite this

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. In 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",

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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. in 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, Spain, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In Barrenechea E, Troncoso A, Gámez JA, Luaces O, Quintián H, Corchado E, Galar M, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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