A travel behaviour study through learning and clustering of fuzzy cognitive maps

Abstract

Transport management and behaviour modelling appears in modern societies because of the importance for all social and economic processes. Using in this field advanced computer techniques like the Artificial Intelligence ones is really relevant from the scientific, economic and social point of view. In this paper we deal with Fuzzy Cognitive Maps as an approach in representing the behaviour and operation of such complex systems. We also show how travellers base their decisions on knowledge of different transport mode properties at different levels of abstraction depending on their perception of available information. These levels correspond to the abstraction hierarchy including different scenarios of traveling, different set of benefits while choosing a specific travel mode, and different situations and attributes related with those benefits. We use learning and clustering of Fuzzy Cognitive Maps to describe travellers’ behaviour and change trends in different abstraction levels.