RideMatcher: Peer-to-Peer Matching of Passengers for Efficient Ridesharing

Abstract

The daily home-office commute of millions of people in crowded cities puts a strain on air quality, traveling time and noise pollution. This is especially problematic in western cities, where cars and taxis have low occupancy with daily commuters. To reduce these issues, authorities often encourage commuters to share their rides, also known as carpooling or ridesharing. To increase the ridesharing usage it is essential that commuters are efficiently matched. In this paper we present RideMatcher, a novel peer-to-peer system for matching car rides based on their routes and travel times. Unlike other ridesharing systems, RideMatcher is completely decentralized, which makes it possible to deploy it on distributed infrastructures, using fog and edge computing. Despite being decentralized, our system is able to efficiently match ridesharing users in near real-time. Our evaluations performed on a dataset with 34,837 real taxi trips from New York show that RideMatcher is able to reduce the number of taxi trips by up to 65%, the distance traveled by taxi cabs by up to 64%, and the cost of the trips by up to 66%.