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.
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- J. Cramer and A. B. Krueger. Disruptive change in the taxi business: The case of uber. 2015.Google Scholar
- 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 Scholar
- M. F. Goodchild. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4):211--221, 2007. Google ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- J. Horpedahl. Ideology über alles' economics bloggers on uber, lyft, and other transportation network companies. Econ Journal Watch, 12(3):360--374, 2015.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- W. Mason and S. Suri. Conducting behavioral research on amazon's mechanical turk. Behavior Research Methods, 44(1):1--23, 2011. Google ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- F. Rechinhheld and W. Sasser. Zero defections: Quality comes to service. Harvard Business Review, 68(5):105--111, 1990.Google Scholar
- 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 ScholarCross Ref
- B. Rogers. The social costs of uber. University of Chicago Law Review Dialogue, 82, 2015.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- R. Quinlan. Data mining tools see5 and c5. 0. 2004.Google Scholar
Index Terms
Analyzing Uber's Ride-sharing Economy
Recommendations
On Ridesharing Competition and Accessibility: Evidence from Uber, Lyft, and Taxi
WWW '18: Proceedings of the 2018 World Wide Web ConferenceRidesharing services such as Uber and Lyft have become an important part of the Vehicle For Hire (VFH) market, which used to be dominated by taxis. Unfortunately, ridesharing services are not required to share data like taxi services, which has made it ...
Peeking Beneath the Hood of Uber
IMC '15: Proceedings of the 2015 Internet Measurement ConferenceRecently, Uber has emerged as a leader in the "sharing economy". Uber is a "ride sharing" service that matches willing drivers with customers looking for rides. However, unlike other open marketplaces (e.g., AirBnB), Uber is a black-box: they do not ...
Show me the way to go home: an empirical investigation of ride-sharing and alcohol related motor vehicle fatalities
In this work, we investigate how the entry of ride-sharing services influences the rate of alcohol related motor vehicle fatalities. While significant debate has surrounded ride-sharing, limited empirical work has been devoted to uncovering the societal ...
Comments