Developing Fuzzy Inference Systems From Qualitative Interviews For Travel Mode Choice In An Agent-Based Mobility Simulation

Both qualitative and quantitative research are integral parts for the understanding of traffic systems, yet it can be difficult to formalize and execute qualitative research results in a technical simulation system in an understandable and flexible manner. This paper presents an approach to systematically construct fuzzy inference systems from socio-scientific data for the application as a decision making component in an agent-based mobility simulation. A general fuzzy inference concept is presented and subsequently applied to statements about travel mode choice and common activities from semi-structured interviews on mobility behavior. It is shown that the inference concept can be used to determine both fuzzy rule base and the linguistic variables and terms from the interviews and that such an inference system can be used successfully in an agent-based mobility simulation.