  We are in the midst of a technology-driven transformation of the urban
mobility landscape. However, unfortunately these new innovations are still
dominated by car-centric personal mobility, which leads to concerns such as
environmental sustainability, congestion, and equity. On the other hand, mass
transit provides a means to move large amounts of travelers very efficiently,
but is not very versatile and depends on an adequate concentration of demand.
In this context, our overarching goal is to explore opportunities for new
technologies such as ride-sharing to integrate with mass transit and provide a
better service. More specifically, we envision a hybrid system that uses
on-demand shuttles in conjunction with mass transit to move passengers
efficiently, and provide an algorithmic framework for operational optimization.
Our approach extends a state-of-the-art trip-vehicle assignment model to the
multi-modal setting, where we develop a new integer-linear programming
formulation to solve the problem efficiently. A comprehensive study covering
five major cities in the United States based on real-world data is carried out
to verify the advantages of such a system and the effectiveness of our
algorithms. We show that our hybrid system provides significant improvements in
comparison to a purely on-demand model by exploiting the efficiencies of the
mass transit system.
