Learning and clustering of fuzzy cognitive maps for travel behaviour analysis

In modern society, more and more attention is given to the increase in public transportation or bike use. In this regard, one of the most important issues is to find and analyse the factors influencing car dependency and the attitudes of people in terms of preferred transport mode. 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 complex systems. This soft-computing technique allows modelling how the travellers make decisions based on their knowledge of different transport modes properties at different levels of abstraction. These levels correspond to the hierarchy perception including different scenarios of travelling, different benefits of choosing a specific travel mode, and different situations and attributes related to those benefits. We use learning and clustering of fuzzy cognitive maps to describe travellers' behaviour and change trends in different abstraction levels. Cluster estimations are done before and after the learning of the maps, in order to compare people's way of thinking if only considering an initial view of a transport mode decision for a daily activity, and when they really have a deeper reasoning process in view of benefits and consequences. 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.