Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling

Maikel León; Lusine Mkrtchyan; Benoît Depaire; Da Ruan; Rafael Bello; Koen Vanhoof

doi:10.1007/978-3-642-33269-2_90

Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling

Maikel León, Lusine Mkrtchyan, Benoît Depaire, Da Ruan, Rafael Bello, Koen Vanhoof

Research output › peer-review

Abstract

Although the individuals' transport behavioural modelling is a complex task, it has a notable social and economic impact. Thus, in this paper Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such systems. This technique allows modelling how the travellers make decisions based on their knowledge of different transport modes properties at different levels of abstraction. We use learning of Fuzzy Cognitive Maps to describe travellers' behaviour and change trends in different abstraction levels. The results of this study will help transportation policy decision makers in better understanding of people's needs and consequently will help them actualizing different policy formulations and implementations.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Pages	718-725
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-33269-2_90
State	Published - 2012
Event	22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne Duration: 11 Sep 2012 → 14 Sep 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7552 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Artificial Neural Networks, ICANN 2012
Country/Territory	Switzerland
City	Lausanne
Period	2012-09-11 → 2012-09-14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-33269-2_90License: Unspecified

Cite this

León, M., Mkrtchyan, L., Depaire, B., Ruan, D., Bello, R., & Vanhoof, K. (2012). Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 1 ed., pp. 718-725). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7552 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-33269-2_90

León, Maikel ; Mkrtchyan, Lusine ; Depaire, Benoît et al. / Learning method inspired on swarm intelligence for fuzzy cognitive maps : Travel behaviour modelling. Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 1. ed. 2012. pp. 718-725 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Although the individuals' transport behavioural modelling is a complex task, it has a notable social and economic impact. Thus, in this paper Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such systems. This technique allows modelling how the travellers make decisions based on their knowledge of different transport modes properties at different levels of abstraction. We use learning of Fuzzy Cognitive Maps to describe travellers' behaviour and change trends in different abstraction levels. The results of this study will help transportation policy decision makers in better understanding of people's needs and consequently will help them actualizing different policy formulations and implementations.",

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León, M, Mkrtchyan, L, Depaire, B, Ruan, D, Bello, R & Vanhoof, K 2012, Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling. in Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7552 LNCS, pp. 718-725, 22nd International Conference on Artificial Neural Networks, ICANN 2012, Lausanne, Switzerland, 2012-09-11. https://doi.org/10.1007/978-3-642-33269-2_90

Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling. / León, Maikel; Mkrtchyan, Lusine; Depaire, Benoît et al.
Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 1. ed. 2012. p. 718-725 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7552 LNCS, No. PART 1).

Research output › peer-review

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AB - Although the individuals' transport behavioural modelling is a complex task, it has a notable social and economic impact. Thus, in this paper Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such systems. This technique allows modelling how the travellers make decisions based on their knowledge of different transport modes properties at different levels of abstraction. We use learning of Fuzzy Cognitive Maps to describe travellers' behaviour and change trends in different abstraction levels. The results of this study will help transportation policy decision makers in better understanding of people's needs and consequently will help them actualizing different policy formulations and implementations.

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León M, Mkrtchyan L, Depaire B, Ruan D, Bello R, Vanhoof K. Learning method inspired on swarm intelligence for fuzzy cognitive maps: Travel behaviour modelling. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 1 ed. 2012. p. 718-725. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-33269-2_90