{"language":[{"iso":"eng"}],"quality_controlled":"1","page":"86-95","publication":"Decision Support Systems","article_type":"original","_id":"2856","status":"public","year":"2017","user_id":"59677","publisher":"Elsevier","keyword":["Taxi ridesharing Collaborative consumption Transportation Open data Sustainability Shared mobility"],"type":"journal_article","citation":{"chicago":"Barann, Benjamin, Daniel Beverungen, and Oliver Müller. “An Open-Data Approach for Quantifying the Potential of Taxi Ridesharing.” Decision Support Systems 99, no. July 2017 (2017): 86–95. https://doi.org/10.1016/j.dss.2017.05.008.","mla":"Barann, Benjamin, et al. “An Open-Data Approach for Quantifying the Potential of Taxi Ridesharing.” Decision Support Systems, vol. 99, no. July 2017, Elsevier, 2017, pp. 86–95, doi:10.1016/j.dss.2017.05.008.","apa":"Barann, B., Beverungen, D., & Müller, O. (2017). An open-data approach for quantifying the potential of taxi ridesharing. Decision Support Systems, 99(July 2017), 86–95. https://doi.org/10.1016/j.dss.2017.05.008","short":"B. Barann, D. Beverungen, O. Müller, Decision Support Systems 99 (2017) 86–95.","ama":"Barann B, Beverungen D, Müller O. An open-data approach for quantifying the potential of taxi ridesharing. Decision Support Systems. 2017;99(July 2017):86-95. doi:10.1016/j.dss.2017.05.008","ieee":"B. Barann, D. Beverungen, and O. Müller, “An open-data approach for quantifying the potential of taxi ridesharing,” Decision Support Systems, vol. 99, no. July 2017, pp. 86–95, 2017, doi: 10.1016/j.dss.2017.05.008.","bibtex":"@article{Barann_Beverungen_Müller_2017, title={An open-data approach for quantifying the potential of taxi ridesharing}, volume={99}, DOI={10.1016/j.dss.2017.05.008}, number={July 2017}, journal={Decision Support Systems}, publisher={Elsevier}, author={Barann, Benjamin and Beverungen, Daniel and Müller, Oliver}, year={2017}, pages={86–95} }"},"intvolume":" 99","issue":"July 2017","date_updated":"2024-04-18T12:59:57Z","title":"An open-data approach for quantifying the potential of taxi ridesharing","abstract":[{"text":"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.","lang":"eng"}],"date_created":"2018-05-24T08:48:58Z","doi":"10.1016/j.dss.2017.05.008","publication_status":"published","author":[{"first_name":"Benjamin","full_name":"Barann, Benjamin","last_name":"Barann"},{"first_name":"Daniel","id":"59677","full_name":"Beverungen, Daniel","last_name":"Beverungen"},{"id":"72849","first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller"}],"volume":99,"department":[{"_id":"526"}]}