Abstract: How to automatically generate realistic and heterogeneous traffic behaviors has been a much needed yet challenging problem for numerous traffic simulation and urban planning applications. In this paper, we propose a novel approach to model heterogeneous traffic behaviors by adapting a well-established personality trait model (i.e., Eysenck's PEN (psychoticism, extraversion and neuroticism) model) into widely used traffic simulation approaches. First, we collected a large amount of user feedback while users watch a variety of computer-generated traffic simulation video clips. Then, we trained regression models to bridge low-level traffic simulation parameters and high-level perceived traffic behaviors (i.e., adjectives according to the PEN model and the three PEN traits). We also conducted an additional user study to validate the effectiveness and usefulness of our approach, in particular, high correlation coefficients and the Pearson values between users’ feedback and our model predictions prove the effectiveness of our approach. Furthermore, our approach can also produce interesting emergent traffic patterns including faster-is-slower effect and sticking-in-a-pin-wherever-there-is-room effect.