  The increasing rate of urbanization has added pressure on the already
constrained transportation networks in our communities. Ride-sharing platforms
such as Uber and Lyft are becoming a more commonplace, particularly in urban
environments. While such services may be deemed more convenient than riding
public transit due to their on-demand nature, reports show that they do not
necessarily decrease the congestion in major cities. One of the key problems is
that typically mobility decision support systems focus on individual utility
and react only after congestion appears. In this paper, we propose socially
considerate multi-modal routing algorithms that are proactive and consider, via
predictions, the shared effect of riders on the overall efficacy of mobility
services. We have adapted the MATSim simulator framework to incorporate the
proposed algorithms present a simulation analysis of a case study in Nashville,
Tennessee that assesses the effects of our routing models on the traffic
congestion for different levels of penetration and adoption of socially
considerate routes. Our results indicate that even at a low penetration (social
ratio), we are able to achieve an improvement in system-level performance.
