Tesone, Alessio (2023) Urban traffic control through Model Predictive Control (MPC) algorithms based on the usage of Macroscopic Fundamental Diagram (MFD). [Tesi di dottorato]

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The increasing motorization is leading to urgency in improving the traffic congestion management techniques in urban cities. In this framework, the network-level control approaches are obtaining growing importance, and the aggregated modelling paradigm of the Macroscopic Fundamental Diagram (MFD) is often used as a fast and reliable tool to monitor the city congestion levels, in particular when the city is divided into different regions, each of which characterized by its own MFD curve. This approach is used in the hierarchical control scheme proposed and tested in this thesis to apply a city-level Route Guidance strategy. The upper level, based on Model Predictive Control (MPC), establishes the optimal split ratios among the regions, while at the lower level, an actuation system spreads the control signal to different drivers by emulating the presence of Variable Message Signs (VMSs) on the infrastructures. The thesis has the objective to investigate and validate the robustness of this approach. Indeed, the framework has been tested in some complex and realistic traffic scenarios, and taking into account different operating conditions (i.e. different parameter settings, fluctuations of the travel demand), behavioural phenomena (e.g. drivers’ compliance), performance indicators (e.g. system effects on both controlled and uncontrolled zones), and objective functions (e.g. throughput maximization vs. emission minimization). In the final part of the work, the effects of emerging technologies of Connected and Autonomous Vehicles (CAVs) are also introduced in the simulation framework, and their impact on the tested controlling schemes is evaluated and discussed.

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