  This paper studies congestion-aware route-planning policies for Autonomous
Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles
provides on-demand mobility under mixed traffic conditions. Specifically, we
first devise a network flow model to optimize the AMoD routing and rebalancing
strategies in a congestion-aware fashion by accounting for the endogenous
impact of AMoD flows on travel time. Second, we capture reactive exogenous
traffic consisting of private vehicles selfishly adapting to the AMoD flows in
a user-centric fashion by leveraging an iterative approach. Finally, we
showcase the effectiveness of our framework with two case-studies considering
the transportation sub-networks in Eastern Massachusetts and New York City. Our
results suggest that for high levels of demand, pure AMoD travel can be
detrimental due to the additional traffic stemming from its rebalancing flows,
while the combination of AMoD with walking or micromobility options can
significantly improve the overall system performance.
