  This paper presents a modeling and optimization framework to study
congestion-aware ride-pooling Autonomous Mobility-on-Demand (AMoD) systems,
whereby self-driving robotaxis are providing on-demand mobility, and users
headed in the same direction share the same vehicle for part of their journey.
Specifically, taking a mesoscopic time-invariant perspective and on the
assumption of a large number of travel requests, we first cast the joint
ride-pooling assignment and routing problem as a quadratic program that does
not scale with the number of demands and can be solved with off-the-shelf
convex solvers. Second, we compare the proposed approach with a significantly
simpler decoupled formulation, whereby only the routing is performed in a
congestion-aware fashion, whilst the ride-pooling assignment part is
congestion-unaware. A case study of Sioux Falls reveals that such a
simplification does not significantly alter the solution and that the decisive
factor is indeed the congestion-aware routing. Finally, we solve the latter
problem accounting for the presence of user-centered private vehicle users in a
case study of Manhattan, NYC, characterizing the performance of the car-network
as a function of AMoD penetration rate and percentage of pooled rides within
it. Our results show that AMoD can significantly reduce congestion and travel
times, but only if at least 40% of the users are willing to be pooled together.
Otherwise, for higher AMoD penetration rates and low percentage of pooled
rides, the effect of the additional rebalancing empty-vehicle trips can be even
more detrimental than the benefits stemming from a centralized routing,
worsening congestion and leading to an up to 15% higher average travel time.
