High-capacity ride-sharing via shortest path clustering on large road networks

Abstract

Ride-sharing has been widely studied in academia and applied in mobility-on-demand systems as a means of reducing the number of cars, congestion, and pollution by sharing empty seats. Solving this problem is challenging on large-scale road networks for the following two reasons: Distance calculation on large-scale road networks is time-consuming, and multi-request allocation and route planning have been proved to be NP-hard problems. In this paper, we propose a clustering-based request matching and route planning algorithm $${Roo}$$ whose basic operations are merging requested trips on road networks. Several requested trips can be merged and served by a vehicle if their shortest paths from origins to destinations are close to each other based on spatiotemporal road network distances. The resultant routes are further refined by introducing meeting points, which can shorten the total traveling distance while keeping matched ride requests satisfied. The $${Roo}$$ algorithm has been evaluated with two real-world taxi trajectory datasets and road networks from New York City and Beijing. The results show that $${Roo}$$ can save up to 50% of mileage by 1000 vehicles serving around 7000 trip requests in New York City between 7:40 and 8:00 am with an average waiting time of 4 minutes.