  This dissertation proposes two solutions for urban traffic control in the
presence of connected and automated vehicles. First a centralized platoon-based
controller is proposed for the cooperative intersection management problem that
takes advantage of the platooning systems and V2I communication to generate
fast and smooth traffic flow at a single intersection.
  Second, a data-driven approach is proposed for adaptive signal control in the
presence of connected vehicles. The proposed system relies on a data-driven
method for optimal signal timing and a data-driven heuristic method for
estimating routing decisions. It requires no additional sensors to be installed
at the intersection, reducing the installation costs compared to typical
settings of state-of-the-practice adaptive signal controllers.
  The proposed traffic controller contains an optimal signal timing module and
a traffic state estimator. The signal timing module is a neural network model
trained on microscopic simulation data to achieve optimal results according to
a given performance metric such as vehicular delay or average queue length. The
traffic state estimator relies on connected vehicles' information to estimate
the traffic's routing decisions. A heuristic method is proposed to minimize the
estimation error. With sufficient parameter tuning, the estimation error
decreases as the market penetration rate (MPR) of connected vehicles grows.
Estimation error is below 30% for an MPR of 10% and it shrinks below 20% when
MPR grows larger than 30%.
  Simulations showed that the proposed traffic controller outperforms Highway
Capacity Manual's methodology and given proper offline parameter tuning, it can
decrease average vehicular delay by up to 25%.
