Modeling of individual differences in driver behavior

Computational transportation is a scientific discipline which uses traffic flow simulation for intervention design and analysis. Realistic traffic flow simulation depends on realistic computational modeling of individual agents such as drivers. Whereas, realistic agent model relies on realistic modeling of microscopic driver behaviors. IDM and MOBIL are considered de-facto car-following and lane change models respectively. All the prominent microscopic models have been developed with engineering perspective i.e. to reproduce perfect driving behavior. Whereas human driving behavior exhibit individual difference and is prone to risks and errors. This study focuses on development of a personality-based model of driver behavior. The parameters that have been modeled to represent driving preferences have been identified. In their existing forms, model parameters could be assigned arbitrary values from a prescribed range to define different driver profiles. This way, theoretically, infinite driver profiles could be created, many of which does not exist in real. Whereas, literature of traffic psychology suggests that there are few prevalent classes of drivers which exhibit certain behavioral patterns. These classes are characterized with the help of human personality. In proposed study, a relationship between personality traits and model parameters have been modeled. This enhancement may reproduce individual differences in driving behaviors.