With the advent of advanced safety features and automated vehicles, driver safety has become critical in situations where the human is expected to disengage or drive partially. It is therefore vital to understand driver profiles in the development of systems that can adapt to the user and to which they can trust. Understanding the driving profile is challenging as it is composed of several factors, including driving style, mood states, and personality traits. To fulfill the purpose of modeling driver profiles, this paper proposed a comprehensive framework. A total of 28 licensed male drivers between the ages of 21 and 40 participated in the study; their driving behavior was recorded to create an integrated dataset. Additionally, mood states and personality traits were collected via surveys. The fuzzy logic inference system identified driving styles based on this integrated dataset. The relationship between driving styles, mood states, and a prediction model using random forest was developed for driving styles and personality types (obtained through clustering). Ultimately, findings from prediction can be utilized in risky driving style detection and driver preference sharing for the Mobility-as-a-Service purpose.