Subash is currently working on Cooperative Control of Connected and Automated Vehicles going through multiple intersections in an urban setting. A centralised control framework is developed where multiple road vehicles pass through an urban road network with multiple intersections. The solution for an optimal comfort travel for each vehicle is developed starting from initial time optimal solutions of individual vehicles and then relaxing the time to account for delays caused by potential conflicts at the intersections and at the connecting roads. Considering the various needs of travel for each vehicle in terms of comfort, energy efficiency and performance, constraints on maximum velocity, maximum acceleration and maximum jerk are taken into account while finding the optimal time solution. Point masses are considered for dynamics for obtaining the initial optimal time solution. Acceleration at the initial and final states of all the connecting roads and intersections are assumed as zero. Solutions are calculated for all routes with the same distance from origin to destination. The vehicles are scheduled by a program based on custom made algorithm using priority-wise philosophy. Both intersection conflicts and connecting road conflicts are resolved by separate programs based on custom made algorithms. The repetitive nature of generating an intersection conflict while resolving a connecting road conflict and vice versa is also addressed. The trajectory for each vehicle is generated after all the conflict resolutions which consists of the position, velocity acceleration and jerk variation with respect to time. Trajectory can only be generated if the initial and final conditions are feasible. In case of infeasibility, the initial optimal solution function is called again to find a feasible initial state or final state for velocity. After scheduling and resolving conflicts the minimum time route is chosen for the vehicles if more than one route is present. Solution for single lane traffic without overtaking is developed. The backbone of the framework is a custom-made analytical solver for optimal time solution, which is much faster than the compared numerical solvers off the shelf.