@INPROCEEDINGS{KootiG2017www,
  author =       {Farshad Kooti and Mihajlo Grbovic and Luca Maria Aiello and Nemanja Djuric and Vladan Radosavljevic and Kristina Lerman},
  title =        {Analyzing the Ride-sharing Economy},
  booktitle =    {Proceedings of the 26th International World Wide Web Conference (Companion WWW2017)},
  year =         {2017},
  pages =        {},
  publisher =    {},
  abstract =     {Uber is a popular ride-sharing application that matches people who need a ride with others who are willing to provide it using their personal vehicles. Uber�s success has fueled the growth of the sharing economy, where consumers and providers exchange services in a peer-to-peer fashion. Despite its growing popularity, few largescale  studies examined Uber specifically, or the factors affecting user participation in the sharing economy in general. We address this gap through a large-scale study of the Uber market that analyzes 59M rides spanning a period of 7 months. These data were extracted from email receipts sent by Uber. Our data set allows us to examine the role of demographics, including age, gender, and race, on participation in the ride-sharing economy. The data is also fine-grained enough to evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches, e.g., riders to drivers of a similar age, can result in a higher driver ratings. Finally, we focus on factors that affect retention and use information from early rides to accurately predict which riders or drivers will become active Uber users.},
  keywords =     {social-dynamics},
}

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