Traffic congestion, dominated by single-occupancy vehicles, reflects not only transportation system inefficiency and negative externalities, but also a sociological state of human isolation. Advances in information and communication technology are enabling the growth of real-time ridesharing to improve system efficiency. While ridesharing algorithms optimize passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time or vehicle-distance traveled), they do not explicitly consider passengers' preference for each other as the matching objective. We propose a preference-based passenger matching model, formulating ridesharing as a maximum stable matching problem. We illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1,000 randomly generated preference orders, and the other considering five sets of group-based preference orders. In both scenarios, compared with efficiency-based matching models, preference-based matching improves the average ranking of paired fellow passenger to the near-top position of people's preference orders with only a small efficiency loss at the individual level, and a moderate loss at the aggregate level. The near-top-ranking results fall in a narrow range even with the random variance of passenger preference as inputs.

Cite as: Zhang, Hongmou, and Jinhua Zhao. 2018. “Mobility Sharing as a Preference Matching Problem.” IEEE Transactions on Intelligent Transportation Systems.