An open-data approach for quantifying the potential of taxi ridesharing

Barann Benjamin, Beverungen Daniel, Müller Oliver

Abstract

Taxi ridesharing (TRS), also known as shared taxi or collective taxi, is an advanced form of public transportation with flexible routing and scheduling that matches at least two separate ride requests with similar spatio-temporal characteristics in real-time to a jointly used taxi, driven by an employed driver without own destination. TRS, therefore, differs from private ridesharing, which refers to sharing of rides among private people. TRS is a more restricted dynamic dial-a-ride problem, which considers the requirements of both multiple passengers and the service provider. Because of the pooled simultaneous utilization of a taxi, TRS is collaborative consumption. [This definition has been pasted from the paper, Section 2.2. References are provided there] (TRS) is an advanced form of urban transportation that matches separate ride requests with similar spatio-temporal characteristics to a jointly used taxi. As collaborative consumption, TRS saves customers money, enables taxi companies to economize use of their resources, and lowers greenhouse gas emissions. We develop a one-to-one TRS approach that matches rides with similar start and end points. We evaluate our approach by analyzing an open dataset of > 5 million taxi trajectories in New York City. Our empirical analysis reveals that the proposed approach matches up to 48.34% of all taxi rides, saving 2,892,036 km of travel distance, 231,362.89 l of gas, and 532,134.64 kg of CO2 emissions per week. Compared to many-to-many TRS approaches, our approach is competitive, simpler to implement and operate, and poses less rigid assumptions on data availability and customer acceptance.