Transportation simulation researchers commonly institute two distinct simulation platforms that are often implemented independent of one another. Traffic Simulation models emulate the macroscopic or mesoscopic behavior of ground vehicles, while Driving Simulators are used to examine microscopic driver behavior within a virtual environment. This research sees the integration of these heterogeneous simulation platforms, which broadens the range of applications for which both simulator types are applicable. The integrated simulation framework has been validated by having several human subjects drive a segment of a signalized arterial in both the artificial environment and on the corresponding real-world roads, during (simulated and actual) rush hour traffic. Various data is collected within the integrated simulation framework, including timestamp, position, velocity, and accelerations, and comparable data is collected (and compared) when the human subjects drive the actual roads. The described framework is then deployed to focus on Human Factors (e.g., driver acceptance and preference) associated with autonomous control features anticipated in next-generation vehicles. In our experiments, participants were asked to assign the headway to a minimum value that they could “tolerateâ€� (i.e., based on workload, confidence, comfort, safety and acceptance). The results demonstrate that most drivers prefer spacing between vehicles by relying on their judgment on distance, rather than headway (time). Future technology will be able to support autonomous vehicle operations, most likely with an evolving trajectory of acceptance, and the human factors element of accepting the technology may lag the deployment of the technology itself. Accordingly, simulator-based efforts to identify human tolerances on the roads have the potential to help to accelerate the adoption of these advanced autonomous technologies. This is the primary motivation for this study, which will help to inform the design of future autonomous vehicle applications, and will serve as a reference point for optimizing the route capacity of next-generation transportation systems.