@article{2856, abstract = {{Taxi ridesharing1 (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.}}, author = {{Barann, Benjamin and Beverungen, Daniel and Müller, Oliver}}, journal = {{Decision Support Systems}}, keywords = {{Taxi ridesharing Collaborative consumption Transportation Open data Sustainability Shared mobility}}, pages = {{86----95}}, title = {{{An open-data approach for quantifying the potential of taxi ridesharing}}}, doi = {{10.1016/j.dss.2017.05.008}}, year = {{2017}}, }