An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.Index Terms-connected automated vehicles (CAV), reinforcement learning (RL), take-over request (ToR), traffic management (TM), transition of control (ToC) NOMENCLATURE AV Automated vehicle CAV Connected automated vehicle CV Connected vehicle LoD Level of demand MDP Markov decision process MRM Minimum risk manoeuvre MV Manual vehicle No-AD No automated driving RL Reinforcement learning RSI Roadside infrastructure TM Traffic management TMC Traffic management center ToC Transition of control ToR Take-over request