A Methodology for Estimating Rideshare Market Potential, using the MIT Campus as a Case

Ridesharing as a mode of travel offers potential solutions to a variety of the transportation sectors toughest challenges including congestion, energy security, GHG emissions and the provision of travel options. However, the transportation literature provides little quantified information on the potential social and environmental benefits that ridesharing could provide. This paper proposes a data driven methodology for estimating the potential of ridesharing at an organizational scale, and seeks to demonstrate the applicability of the methodology using the Massachusetts Institute of Technology commuting population as a case. The methodology seeks to improve upon previous research by comparing the relative importance of trip characteristics in the determination of rideshare arrangements vs. the importance of human preferences. MIT-specific results suggest that between 50% and 77% of the commuting population could rideshare on a maximum-effort day. These are values significantly higher than the 8% that currently choose to rideshare. Maximum achievable VMT reductions from daily ridesharing are between 9% and 27%. Through the manipulation of various trip characteristic assumptions, the disparity between the modeled results and observed behavior suggest that human preferences have a substantial influence on rideshare participation. [Author's Note: This paper originally began as a self-directed project under the supervision of an MIT staff member. The results have been sufficiently well received that some of the content of this paper will constitute a chapter of my program thesis. In the end, it will represent no more than one of eight chapters in a final thesis. In other words, it will not constitute the bulk of my final thesis]