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Analyzing Uber's Ride-sharing Economy

Published:03 April 2017Publication History

ABSTRACT

Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist few studies that examine large-scale Uber data, or in general the factors affecting user participation in the sharing economy. We address this gap through a study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months. The data were extracted from email receipts sent by Uber collected on Yahoo servers, allowing us to examine the role of demographics (e.g., age and gender) on participation in the ride-sharing economy. In addition, we 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 higher driver ratings. Finally, we focus on factors that affect retention and use information from earlier rides to accurately predict which riders or drivers will become active Uber users.

References

  1. This is your brain on uber. http://www.npr.org/2016/05/17/478266839/this-is-your-brain-on-uber. Accessed: 2016-05-27.Google ScholarGoogle Scholar
  2. M. Buhrmester, T. Kwang, and S. D. Gosling. Amazon's mechanical turk a new source of inexpensive, yet high-quality, data? Perspectives on psychological science, 6(1):3--5, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  3. L. Chen, A. Mislove, and C. Wilson. Peeking beneath the hood of uber. In Proc. of IMC, pp. 495--508, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Cramer and A. B. Krueger. Disruptive change in the taxi business: The case of uber. 2015.Google ScholarGoogle Scholar
  5. B. G. Edelman and D. Geradin. Efficiencies and regulatory shortcuts: How should we regulate companies like airbnb and uber? Harvard Business School NOM Unit Working Paper, (16-026), 2015.Google ScholarGoogle Scholar
  6. M. F. Goodchild. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4):211--221, 2007. Google ScholarGoogle ScholarCross RefCross Ref
  7. J. V. Hall and A. B. Krueger. An analysis of the labor market for uber's driver-partners in the united states. Princeton University Industrial Relations Section Working Paper, 587, 2015.Google ScholarGoogle Scholar
  8. J. Hamari, M. Sjöklint, and A. Ukkonen. The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Horpedahl. Ideology über alles' economics bloggers on uber, lyft, and other transportation network companies. Econ Journal Watch, 12(3):360--374, 2015.Google ScholarGoogle Scholar
  10. T. Ikkala and A. Lampinen. Monetizing network hospitality: Hospitality and sociability in the context of airbnb. In Proc. CSCW, pp. 1033--1044, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Kittur, E. H. Chi, and B. Suh. Crowdsourcing user studies with mechanical turk. In Proc. of CHI, pp. 453--456, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. K. Lee, D. Kusbit, E. Metsky, and L. Dabbish. Working with machines: The impact of algorithmic and data-driven management on human workers. In Proc. CHI, pp. 1603--1612, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Lindqvist, J. Cranshaw, J. Wiese, J. Hong, and J. Zimmerman. I'm the mayor of my house: Examining why people use foursquare - a social-driven location sharing application. In Proc. CHI, pp. 2409--2418, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. W. Mason and S. Suri. Conducting behavioral research on amazon's mechanical turk. Behavior Research Methods, 44(1):1--23, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  15. A. Noulas, V. Salnikov, R. Lambiotte, and C. Mascolo. Mining open datasets for transparency in taxi transport in metropolitan environments. EPJ Data Science, 4(1):1--19, 2015. Google ScholarGoogle ScholarCross RefCross Ref
  16. G. Quattrone, D. Proserpio, D. Quercia, L. Capra, and M. Musolesi. Who benefits from the sharing economy of airbnb? In Proc. of WWW, pp. 1385--1394. 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. Rayle, D. Dai, N. Chan, R. Cervero, and S. Shaheen. Just a better taxi? a survey-based comparison of taxis, transit, and ridesourcing services in san francisco. Transport Policy, 45:168--178, 2016. Google ScholarGoogle ScholarCross RefCross Ref
  18. F. Rechinhheld and W. Sasser. Zero defections: Quality comes to service. Harvard Business Review, 68(5):105--111, 1990.Google ScholarGoogle Scholar
  19. Y. Richter, E. Yom-Tov, and N. Slonim. Predicting customer churn in mobile networks through analysis of social groups. In Proc. of SDM, volume 2010, pp. 732--741. SIAM, 2010. Google ScholarGoogle ScholarCross RefCross Ref
  20. B. Rogers. The social costs of uber. University of Chicago Law Review Dialogue, 82, 2015.Google ScholarGoogle Scholar
  21. V. Salnikov, R. Lambiotte, A. Noulas, and C. Mascolo. Openstreetcab: Exploiting taxi mobility patterns in new york city to reduce commuter costs. arXiv preprint arXiv:1503.03021, 2015.Google ScholarGoogle Scholar
  22. S. A. Sheppard, A. Wiggins, and L. Terveen. Capturing quality: Retaining provenance for curated volunteer monitoring data. In Proc. CSCW, pp. 1234--1245, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. R. Teodoro, P. Ozturk, M. Naaman, W. Mason, and J. Lindqvist. The motivations and experiences of the on-demand mobile workforce. In Proc. of CSCW, pp. 236--247, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. G. Zervas, D. Proserpio, and J. Byers. The rise of the sharing economy: Estimating the impact of airbnb on the hotel industry. Boston U. School of Management Research Paper, (2013--16), 2015.Google ScholarGoogle Scholar
  25. G. Zervas, D. Proserpio, and J. W. Byers. The impact of the sharing economy on the hotel industry: Evidence from airbnb's entry into the texas market. In Proc. of EC, pp. 637--637, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. R. Quinlan. Data mining tools see5 and c5. 0. 2004.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion
      April 2017
      1738 pages
      ISBN:9781450349147

      Publisher

      International World Wide Web Conferences Steering Committee

      Republic and Canton of Geneva, Switzerland

      Publication History

      • Published: 3 April 2017

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      WWW '17 Companion Paper Acceptance Rate164of966submissions,17%Overall Acceptance Rate1,899of8,196submissions,23%

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