Autonomous mobility-on-demand (AMoD) systems, centrally coordinated fleets of self-driving vehicles, offer a promising alternative to traditional ride-hailing by improving traffic flow and reducing operating costs. Centralized control in AMoD systems enables two complementary routing strategies: balanced routing, which distributes traffic across alternative routes to ease congestion, and staggered routing, which delays departures to smooth peak demand over time. In this work, we introduce a unified framework that jointly optimizes both route choices and departure times to minimize system travel times. We formulate the problem as an optimization model and show that our congestion model yields an unbiased estimate of travel times derived from a discretized version of Vickrey's bottleneck model. To solve large-scale instances, we develop a custom metaheuristic based on a large neighborhood search framework. We assess our method through a case study on the Manhattan street network using real-world taxi data. In a setting with exclusively centrally controlled AMoD vehicles, our approach reduces total traffic delay by up to 25 percent and mitigates network congestion by up to 35 percent compared to selfish routing. We also consider mixed-traffic settings with both AMoD and conventional vehicles, comparing a welfare-oriented operator that minimizes total system travel time with a profit-oriented one that optimizes only the fleet's travel time. Independent of the operator's objective, the analysis reveals a win-win outcome: across all control levels, both autonomous and non-autonomous traffic benefit from the implementation of balancing and staggering strategies.
Coppola et al. (Tue,) studied this question.