Advances in information and communications technologies are enabling the growth of real-time ride sharing—whereby drivers and passengers or fellow passengers are paired up on car trips with similar origin-destinations and proximate time windows—to improve system efficiency by moving more people in fewer cars. Lesser known, however, are the opportunities of shared mobility as a tool to foster and strengthen human interactions.

    In this dissertation, I used preference as a lens to investigate the social interaction in mobility sharing, including how the interpersonal preference in mobility sharing can be understood, utilized and reshaped. More specifically, I answered the questions of how preference could be used to match fellow passengers and to improve trip experiences; how gender, one of the key factors may contribute to this preference; and in the reverse direction, if there are factors in the preference which are unrespectable and need to be changed, whether mobility sharing can be used as a tool to change it, and improve the integration of cities. Besides, I also studied how time flexibility of trips can be incorporated into mobility sharing models to reduce congestion.

    For policy makers and planners, this dissertation could partially answer or provide a framework of analysis to the following questions. 1) How could preference in mobility sharing services be used or misused? What is the efficiency trade-off, and how to regulate the use of it? 2) What factors may impact the preference for fellow passengers? Are the preference factors respectable, and what factors should be included/excluded in the mobility sharing services from a regulation perspective? 3) How can mobility sharing be actively used as a tool to encourage more social interaction, especially across different social groups? What is the short-term cost, and the long-term benefit?

    For the system designers of mobility sharing services, this dissertation can be used as a reference for the development of a preference-based mobility sharing platform. The following questions have been traced, and the methods can be improved when more data are available to the system designers. 1) If preference is to be used, what input data are needed, and how they need to be processed for the preference-matching model? 2) What preference factors should be included in the system design, what factors should be used with caution, and what factors should be eliminated? 3) If time flexibility of trips can be included in the system design, how much congestion can be reduced? What system design is needed in order to achieve this congestion reduction?