  One of the potential capabilities of Connected and Autonomous Vehicles (CAVs)
is that they can have different route choice behavior and driving behavior
compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow
with multiple classes of route choice behavior. Therefore, it is crucial to
solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs
and HDVs. Few studies have tried to solve this problem; however, most used
analytical solutions, which are challenging to implement in real and large
networks (especially in dynamic cases). Also, studies in implementing
simulation-based methods have not considered all of CAVs' potential
capabilities. On the other hand, several different (conflicting) assumptions
are made about the CAV's route choice behavior in these studies. So, providing
a tool that can solve the multiclass TAP of mixed traffic under different
assumptions can help researchers to understand the impacts of CAVs better. To
fill these gaps, this study provides an open-source solution framework of the
multiclass simulation-based traffic assignment problem for mixed traffic of
CAVs and HDVs. This model assumes that CAVs follow system optimal principles
with rerouting capability, while HDVs follow user equilibrium principles.
Moreover, this model can capture the impacts of CAVs on road capacity by
considering distinct driving behavioral models in both micro and meso scales
traffic simulation. This proposed model is tested in two case studies which
shows that as the penetration rate of CAVs increases, the total travel time of
all vehicles decreases.
