A two-level framework for dynamic route planning and trajectory optimization of connected and automated vehicles in road networks

This study proposes a two-level optimization control framework for connected and automated vehicles (CAVs) to minimize fuel consumption and travel delay by integrating network-level dynamic route planning (upper level) and vehicle-level trajectory optimization (lower level). At the upper level, the optimal route is updated whenever a CAV enters a new link. To account for lane-specific traffic dynamics, a topological transformation method is introduced, distinguishing lanes by direction and incorporating lane impedance based on traffic density and turning movements. The Floyd–Warshall algorithm is employed to determine the dynamic optimal route within this transformed network structure. At the lower level, an optimization model is formulated to generate an ideal vehicle trajectory within the optimization zone of a link. The vehicle’s initial velocity is set to ensure adequate space for safe maneuvering. The optimal route from the upper level serves as an input for defining the vehicle’s terminal velocity based on its direction, forming a boundary constraint for the trajectory optimization model. By coordinating network-level routing and vehicle-level motion control, the proposed two-level framework mitigates sharp acceleration and deceleration, reducing unnecessary stops at signalized intersections. Numerical experiments and sensitivity analyses demonstrate the effectiveness of the framework in improving network performance by reducing both fuel consumption and travel delay.