Traffic control and management in transportation networks considering connected and autonomous vehicles

The emerging Connected and Automated Vehicles (CAVs) technologies are acknowledged to hold a great promise to mitigate challenging problems in the current transportation networks. Particularly, the connectivity and automation features of CAVs can enable advanced control methods regarding the movements of individual vehicles, which in turn can be leveraged for efficient traffic congestion management. To this end, many works in the literature have focused on the joint control strategy for an isolated intersection while the system-wide investigation for the joint trajectory planning of vehicles and signal control in transportation networks with multiple intersections has not been thoroughly studied. Therefore, this thesis aims to fill this gap by developing an advanced control framework that combines a design of traffic signal timings and a detailed trajectory control policy for individual vehicles in large-scale signalized transportation networks in the presence of CAVs.