Optimal and adaptive controller design for motorway traffic with connected and automated vehicles

Connected vehicles control has brought the opportunity of using a higher level of Traffic Management Center (TMC) to investigate the driving-related terms of congestion avoidance, and safety and pleasantness of driving. Apparently, the efficiency of the terms is improved by the true identification of the assumptions made by the TMC. This thesis adopts microscopic and macroscopic investigations in a motorway considering the effects of lane-changing and ramp metering that exploits the presence of connected vehicles. Microscopically, a car-following behavior in practical scenarios may witness some special situations affected by the vehicles in the adjacent lane that diverts the behavior from the conventional car-following conditions, e.g., a Follower Vehicle (FV) behavior witnesses a lane changing.Addressing the complex situation, human drivers practically may anticipate the surrounding vehicles before lane-changing to make safe control decisions for the behavior. Thus,we design a novel human-like fuzzy controller for the FV witnessing the exiting lane-changer in the complex behavior subject to comfort and safety. The approach will improve the conventional car following Advance Driving Assistant Systems (ADAS), towards a fully automated car-following that enhances the connected vehicles' frameworks. From a macroscopic perspective, few studies may actually have a direct impact on traffic flow control among the wide range of available systems, while the majority of them aim at primarily improving safety or driver convenience. To address these issues, we bridge conventional traffic control, i.e., ramp-metering, with lane-changing control enabled by vehicle automation via a novel feedback-based integrated control strategy. We envision that, if lane-changing control capabilities are implemented in conjunction with more traditional traffic management strategies, such asramp-metering, in an integrated fashion, the resulting effectiveness, in terms of traffic performance, would be further increased. This allows us for rigorous investigations on the generalisability and robustness of the methodology for different network typologies and under different disturbances. However, the integrated lane-changing and ramp-metering control approach assume predefined constant set-points, typically critical density, which is supposed tobe determined based on, e.g., historical data. On the contrary, even if a set-point is known, it may not always be optimal due to possible changes in traffic behavior characteristics, such as, e.g., adifferent traffic composition. Thus to complete the control loop for general usage, we need to designan adaptive estimator integrable to the control strategy capable to update accurate critical density (constant or time-varying)