Journal Article
Mobility Sharing as a Preference Matching Problem

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 most ridesharing algorithms optimize fellow passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time, or vehicle-distance traveled), very few explicitly consider passengers' preference for their peers as the matching objective. The existing literature either considers the bipartite driver-passenger matching problem, which is structurally different from the monopartite passenger-passenger matching, or only considers the passenger-passenger problem in a simplified one-origin-multiple-destination setting. We formulate a general monopartite passenger matching model in a road network and illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1000 randomly generated preference orders and the other considering four 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.

Title
Publication TypeJournal Article
Year of Publication2018
AuthorsZhang H, Zhao J
JournalIEEE Transactions on Intelligent Transportation Systems
Keywordsmatching, Mobility sharing, preference, social interaction
Abstract

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 most ridesharing algorithms optimize fellow passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time, or vehicle-distance traveled), very few explicitly consider passengers' preference for their peers as the matching objective. The existing literature either considers the bipartite driver-passenger matching problem, which is structurally different from the monopartite passenger-passenger matching, or only considers the passenger-passenger problem in a simplified one-origin-multiple-destination setting. We formulate a general monopartite passenger matching model in a road network and illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1000 randomly generated preference orders and the other considering four 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.

DOI10.1109/TITS.2018.2868366