Funding: MIT Institute of Data, System and Society (IDSS) Research Fund 2016

Traffic congestion, dominated by single-occupancy vehicles, reflects not only the inefficiency of the transportation system, but also a sociological state of human isolation. Advances in information and communications technology are enabling the growth of real-time ride sharing—whereby passengers are paired up on car trips with similar origins and destinations and proximate time windows—to improve system efficiency by moving more people in fewer cars. Lesser known, however, are the opportunities presented by shared mobility as a tool to foster and strengthen human interactions. In contrast to typical social interactions in public or private space (meeting rooms, streets, public squares, living rooms, etc.) the nature of shared car rides is impromptu, holds passengers "captive" for a considerable duration, and is remarkably more intimate than other sorts of transportation journeys, representing a unique juxtaposition of spontaneity and intensity. In these ways, it is distinct from mass transit modes such as buses and trains, where most passengers refrain from engaging with each other. Funded by the MIT Institute of Data, System and Society, this research examines the bi-directional connections between shared mobility as a transportation technology, and as an emerging mode of human interaction: how the unique temporal-spatial setting of ride sharing may (a) contribute to or preclude its growth in travel mode share, and (b) affect the quality and quantity of meaningful human interactions in the urban environment. 

Currently, passenger-matching algorithms are based upon the criterion of efficiency, namely maximizing occupancy and minimizing travel distance, while neglecting the sociological aspects of mobility sharing. In this IDSS research, we study mobility sharing from the perspective of human interactions, define and measure individuals’ preferences for fellow passengers, and develop matching algorithms that respect both the transportation network optimization and the individuals’ preferences (or lack of) for human interactions, in order to realize the societal benefits of interactions complementing the environmental and economic benefits of reduced congestion.