A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have
different route choice behavior and driving behavior compared with 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 the mixed traffic flow 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. 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 the mixed traffic flow 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. This proposed model is tested in two case studies.
Researchers and decision-makers can implement this model in planning and operating strategies to leverage
the advantages of CAVs.