Human factor models has been an increasingly more popular topic in traffic models. The objective of these models vary, from simulating cooperative driving to understanding the behaviour of distracted drivers. Regardless of these diverse objectives, the reasons motivating these researches boil down to one single reason, safety. By better understanding human behaviour it should be possible to increase the safety of drivers on the road. One model which offers a systematic approach of studying human factors is the task-capability interface (TCI) model, it models the underlying human thought process and uses it as a proxy for other human factors. This has made the model quite successful in replicating various human factors, including distractions. Multiple papers have studied distractions using the TCI model as a tool but they all had their own specific approach to distractions. This leads to the identified gap in literature: how can distraction be systematically modelled in a TCI traffic model.\\
To fill this gap a distraction framework has been developed. This framework relies on the low-level characteristics of distractions and separates their lifecycle into three stages. These stages are the distraction trigger, intensity and effect. In order to verify if this framework is capable of systematically and accurately modelling distractions it was subjected to a validation test. To this end the distraction framework was incorporated into the Multi-scale model, which was found to be the most fitting TCI model, this resulted in the Distraction model. The new Distraction model was subsequently calibrated with a genetic algorithm to two different datasets with vastly different distraction, a continuous mental-visual distraction and a spontaneous auditory distraction. The results were compared to the results of specialized Distraction models.\\
The validation test results show that the Distraction model has shown limited improvements over the specialized baseline models and that most of the time its performance is equivalent. To be more specific the Distraction model is significantly better at estimating the headway of drivers compared to the baseline models when calibrating for single drivers. That said when it's used as a calibrated model it loses this edge and its performance is fully equivalent to the baseline models. With these results it can be concluded that the distraction framework functions as intended. Despite the limited amount of different distractions in the validation test it has shown that it is capable of systematically modelling distraction on a similar level as other specialized models. This also shows that the main benefits of the framework are its systematic approach and flexibility and not its performance capabilities.