Boosting Ride Sharing With Alternative Destinations

People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can he performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the topk most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can he saved and how the ranking function can be tuned to enforce pro-environment behaviors.