Embedding a neuro-fuzzy mode choice tool in intelligent agents

Increasing road traffic levels in urban areas require actions and policies to manage and control the number of road users. Travelers' choices of transport modes, particularly private cars, that generate the main share of road traffic levels, depend on many factors, which include both personal preferences and level-of-service variables. Understanding how travelers choose transport modes according to the above factors is an important challenge in order to adopt the most suitable policies and facilitate a sustainable mobility. In the literature, behavioral models have been mainly proposed in order to both estimate mode choice percentages and capture travel behaviors by suitable estimation of some parameters associated to the above factors. However, behavior is complex in itself and the mechanisms underlying user behavior might be difficult to be captured by traditional models. In this paper, a neuro-fuzzy approach is proposed to extract mode choice decision rules by evaluating different sets of rules and different membership functions of the neuro-fuzzy model. Particularly, to determine which inputs are the most relevant in such decision process, fuzzy curves and surfaces have been considered in order to take into account nonlinear effects. The neuro-fuzzy model proposed in this paper has been thought to be embedded in an agent-based methodological framework where user agents - representing travelers - make travel choices based on the rules learnt by means of the neuro-fuzzy system.