  In this paper, we introduce a hierarchical decision-making framework for
emerging mobility systems. Despite numerous studies focusing on optimizing
vehicle flow, practical feasibility has often been overlooked. To address this
gap, we present a route-recovery method and energy-optimal trajectory planning
tailored for connected and automated vehicles (CAVs) to ensure the realization
of optimal flow. Our approach identifies the optimal vehicle flow to minimize
total travel time while considering consistent mobility demands in urban
settings. We deploy a heuristic route-recovery algorithm that assigns routes to
CAVs and departure/arrival time at each road segment. Furthermore, we propose
an efficient coordination method that rapidly solves constrained optimization
problems by flexibly piecing together unconstrained energy-optimal
trajectories. The proposed method has the potential to effectively generate
optimal vehicle flow, contributing to the reduction of travel time and energy
consumption in urban areas.
