TY - JOUR
T1 - Tackling Travel Behaviour
T2 - An approach based on Fuzzy Cognitive Maps
AU - León, Maikel
AU - Nápoles, Gonzalo
AU - Bello, Rafael
AU - Mkrtchyan, Lusine
AU - Depaire, Benoît
AU - Vanhoof, Koen
N1 - © 2013 Copyright the authors.
PY - 2013/12
Y1 - 2013/12
N2 - Although the individuals' transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers' behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people's needs in a better way, consequently will help them actualising different policy formulations and implementations.
AB - Although the individuals' transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers' behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people's needs in a better way, consequently will help them actualising different policy formulations and implementations.
KW - Aggregation
KW - Clustering
KW - Fuzzy Cognitive Maps
KW - Particle Swarm Optimisation
KW - Travel Behaviour
UR - http://www.scopus.com/inward/record.url?scp=84880012963&partnerID=8YFLogxK
U2 - 10.1080/18756891.2013.816025
DO - 10.1080/18756891.2013.816025
M3 - Article
AN - SCOPUS:84880012963
SN - 1875-6891
VL - 6
SP - 1012
EP - 1039
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
IS - 6
ER -