Multimodal Connections between Dockless Bikesharing and Ride-Hailing: An Empirical Study in New York City

Abstract

Motivated by the meteoric rise in the adoption of both ride-hailing services (DiDi, Uber, Lyft, etc.) and dockless bikesharing services (Ofo, Mobike, LimeBike, etc.), we propose a multimodal system where passengers ride a dockless bikeshare to/from hubs where they switch modes to/from a carpool. The proposed mutlimodal system is a generalization of the existing Uber ExpressPool service. The goal of this paper is to test empirically the feasibility of the proposed multimodal system. We accomplish this goal with the aid of time-stamped taxi origin/destination data from New York City. The analysis has two steps: network design and trip assignment. First, we identify 17 carpool hub locations with a coverage of 1 km to capture all taxi trip demand within Manhattan during peak hours. After designing the network, we then assign trips to carpools, within each hub, that have similar trip start times and destinations. We formulate the assignment problem as an offline matching algorithm on a bipartite graph. We found that over 80 percent of all trips can be assigned to carpools at almost all hubs. Compared to a single-modal system, the multimodal system served the same number of passengers with 40 percent fewer taxis. We found the matching rate to be consistent for every month in 2015. These results provide initial evidence that multimodal connections between ride-hailing and dockless bikesharing are feasible, reduces passenger trip times, and decreases road congestion.