Teleoperation is a fundamental feature for the development of autonomous vehicles. Not only offers it the possibility of an efficient fallback solution for emerging situations that the autonomous vehicle cannot resolve itself but also it potentially offers new business models for transportation service providers. Fundamental to these considerations, however, is the performance of the communication network and the resulting parameters such as latency and bandwidth. These quantities have a critical impact on the ability of a human to perceive the environment, assess the situation and remotely control the vehicle safely and reliably. In this paper, a framework for simulating and evaluating the impact of network parameters on the performance of a remote human driver is presented. Using an example scenario of a shuttle bus route, the effects of latency for the teleoperation use case is evaluated. The local network and the impact on the human driver are modeled in SUMO (Simulation of Urban MObility) and OMNEST. The latency network simulation is validated against real measurement data. The car-following model Extended Intelligent Driver Model (EIDM) and its human driver model are extended so that latency effects can be quantified. The proposed approach has multiple advantages, on the one hand, it allows to evaluate the network suitability for the teleoperation use case. On the other hand, it limit the number of variables to be evaluated in e.g. user studies.