Ridesharing is an emerging travel mode that reduces the total amount of traffic on the road by combining people's travels together. While present ridesharing algorithms are trip-based, this paper aims to achieve significantly higher matching chances by a novel, activity-based algorithm. The algorithm expands the potential destination choice set by considering alternative destinations that are within given space-time budgets and would provide a similar activity function as the originals. In order to address the increased combinatorial complexity of trip chains, the paper introduces an efficient space-time filter on the foundations of time geography to search for accessible resources. Globally optimal matching is achieved by binary linear programming. The ridesharing algorithm is tested with a series of realistic scenarios of different population sizes. The encouraging results demonstrate that the matching rate by activity-based ridesharing is significantly increased from the baseline scenario of traditional trip-based ridesharing.
['Yaoli Wang', 'Ronny J. Kutadinata', 'Stephan Winter']
Activity-based ridesharing: increasing flexibility by time geography