{"publication_status":"published","status":"public","doi":"10.1016/j.dss.2017.05.008","type":"journal_article","date_updated":"2024-04-18T12:59:57Z","issue":"July 2017","language":[{"iso":"eng"}],"date_created":"2018-05-24T08:48:58Z","intvolume":"        99","department":[{"_id":"526"}],"user_id":"59677","year":"2017","author":[{"last_name":"Barann","full_name":"Barann, Benjamin","first_name":"Benjamin"},{"full_name":"Beverungen, Daniel","id":"59677","last_name":"Beverungen","first_name":"Daniel"},{"first_name":"Oliver","last_name":"Müller","full_name":"Müller, Oliver","id":"72849"}],"publisher":"Elsevier","title":"An open-data approach for quantifying the potential of taxi ridesharing","volume":99,"keyword":["Taxi ridesharing Collaborative consumption Transportation Open data Sustainability Shared mobility"],"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"}],"publication":"Decision Support Systems","page":"86-95","quality_controlled":"1","citation":{"short":"B. Barann, D. Beverungen, O. Müller, Decision Support Systems 99 (2017) 86–95.","chicago":"Barann, Benjamin, Daniel Beverungen, and Oliver Müller. “An Open-Data Approach for Quantifying the Potential of Taxi Ridesharing.” <i>Decision Support Systems</i> 99, no. July 2017 (2017): 86–95. <a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">https://doi.org/10.1016/j.dss.2017.05.008</a>.","ama":"Barann B, Beverungen D, Müller O. An open-data approach for quantifying the potential of taxi ridesharing. <i>Decision Support Systems</i>. 2017;99(July 2017):86-95. doi:<a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">10.1016/j.dss.2017.05.008</a>","mla":"Barann, Benjamin, et al. “An Open-Data Approach for Quantifying the Potential of Taxi Ridesharing.” <i>Decision Support Systems</i>, vol. 99, no. July 2017, Elsevier, 2017, pp. 86–95, doi:<a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">10.1016/j.dss.2017.05.008</a>.","bibtex":"@article{Barann_Beverungen_Müller_2017, title={An open-data approach for quantifying the potential of taxi ridesharing}, volume={99}, DOI={<a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">10.1016/j.dss.2017.05.008</a>}, number={July 2017}, journal={Decision Support Systems}, publisher={Elsevier}, author={Barann, Benjamin and Beverungen, Daniel and Müller, Oliver}, year={2017}, pages={86–95} }","apa":"Barann, B., Beverungen, D., &#38; Müller, O. (2017). An open-data approach for quantifying the potential of taxi ridesharing. <i>Decision Support Systems</i>, <i>99</i>(July 2017), 86–95. <a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">https://doi.org/10.1016/j.dss.2017.05.008</a>","ieee":"B. Barann, D. Beverungen, and O. Müller, “An open-data approach for quantifying the potential of taxi ridesharing,” <i>Decision Support Systems</i>, vol. 99, no. July 2017, pp. 86–95, 2017, doi: <a href=\"https://doi.org/10.1016/j.dss.2017.05.008\">10.1016/j.dss.2017.05.008</a>."},"_id":"2856","article_type":"original"}