Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers

Published Online:https://doi.org/10.1287/isre.2020.0946

References

  • Ackerberg DA (2003) Advertising, learning, and consumer choice in experience good markets: An empirical examination. Internat. Econom. Rev. 44(3):1007–1040.CrossrefGoogle Scholar
  • Andrews M, Luo X, Fang Z, Ghose A (2015) Mobile ad effectiveness: Hyper-contextual targeting with crowdedness. Marketing Sci. 35(2):218–233.LinkGoogle Scholar
  • Aral S, Brynjolfsson E, Van Alstyne M (2012) Information, technology, and information worker productivity. Inform. Systems Res. 23(3, pt. 2):849–867.Google Scholar
  • Athey S (2015) Machine learning and causal inference for policy evaluation. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 5–6.Google Scholar
  • Aurenhammer F (1991) Voronoi diagrams: A survey of a fundamental geometric data structure. ACM Comput. Surveys 23(3):345–405.CrossrefGoogle Scholar
  • Bakos Y, Katsamakas E (2008) Design and ownership of two-sided networks: Implications for internet platforms. J. Management Inform. Systems 25(2):171–202.CrossrefGoogle Scholar
  • Balafoutas L, Beck A, Kerschbamer R, Sutter M (2013) What drives taxi drivers? A field experiment on fraud in a market for credence goods. Rev. Econom. Stud. 80(3):876–891.CrossrefGoogle Scholar
  • Bargh JA, Chartrand TL (1999) The unbearable automaticity of being. Amer. Psych. 54(7):462–479.CrossrefGoogle Scholar
  • Bhargava HK, Choudhary V (2004) Economics of an information intermediary with aggregation benefits. Inform. Systems Res. 15(1):22–36.LinkGoogle Scholar
  • Brennan A (2014) Taxi & Limousine Services in the US. IBISWorld Industry Report 48533, IBIS World, Los Angeles. Available at https://www.ibisworld.com/united-states/market-research-reports/taxi-limousine-services-industry/.Google Scholar
  • Buchholz N (2015) Spatial equilibrium, search frictions and efficient regulation in the taxi industry. Working paper, The University of Texas at Austin, Austin.Google Scholar
  • Buchholz N, Shum M, Xu H (2016) Semiparametric estimation of dynamic discrete choice models. Working paper, The University of Texas at Austin, Austin.Google Scholar
  • Camerer C, Babcock L, Loewenstein G, Thaler R (1997) Labor supply of New York City cabdrivers: One day at a time. Quart. J. Econom. 112(2):407–441.CrossrefGoogle Scholar
  • Chen MK, Sheldon M (2016) Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform. Proc. 2016 ACM Conf. Econom. Comput. (EC '16) (ACM, New York), 455.Google Scholar
  • Ching AT, Erdem T, Keane MP (2013) Invited paper: Learning models: An assessment of progress, challenges, and new developments. Marketing Sci. 32(6):913–938.LinkGoogle Scholar
  • Choi J, Bell DR, Lodish LM (2012) Traditional and IS-enabled customer acquisition on the internet. Management Sci. 58(4):754–769.LinkGoogle Scholar
  • Cramer J, Krueger AB (2016) Disruptive change in the taxi business: The case of Uber. Amer. Econom. Rev. 106(5):177–182.CrossrefGoogle Scholar
  • Crawford VP, Meng J (2011) New York City cab drivers’ labor supply revisited: Reference-dependent preferences with rational-expectations targets for hours and income. Amer. Econom. Rev. 101(5):1912–1932.CrossrefGoogle Scholar
  • DeGroot MH (2005) Optimal Statistical Decisions, vol. 82 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Dubé JP, Sudhir K, Ching A, Crawford GS, Draganska M, Fox JT, Hartmann W, et al.. (2005) Recent advances in structural econometric modeling: Dynamics, product positioning and entry. Marketing Lett. 16(3–4):209–224.CrossrefGoogle Scholar
  • Erdem T, Keane MP (1996) Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Sci. 15(1):1–20.LinkGoogle Scholar
  • Farber HS (2005) Is tomorrow another day? The labor supply of New York City cabdrivers. J. Political Econom. 113(1):46–82.CrossrefGoogle Scholar
  • Farber HS (2008) Reference-dependent preferences and labor supply: The case of New York City taxi drivers. Amer. Econom. Rev. 98(3):1069–1082.CrossrefGoogle Scholar
  • Farber HS (2015) Why you can’t find a taxi in the rain and other labor supply lessons from cab drivers. Quart. J. Econom. 130(4):1975–2026.Google Scholar
  • Francalanci C, Galal H (1998) Information technology and worker composition: Determinants of productivity in the life insurance industry. Management Inform. Systems Quart. 22(2):227–241.CrossrefGoogle Scholar
  • Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM) (Association for Computing Machinery, New York, NY, USA), 899–908.Google Scholar
  • Ghose A, Li B, Liu S (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.LinkGoogle Scholar
  • Greenwood BN, Wattal S (2017) Show me the way to go home: An empirical investigation of ride sharing and alcohol-related motor vehicle homicide. MIS Quart. 41(1):163–187.Google Scholar
  • Guda H, Subramanian U (2019) Your Uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives. Management Sci. 65(5):1995–2014.AbstractGoogle Scholar
  • Haggag K, McManus B, Paci G (2017) Learning by driving: Productivity improvements by New York City taxi drivers. Amer. Econom. J. Appl. Econom. 9(1):70–95.CrossrefGoogle Scholar
  • Hall J, Kendrick C, Nosko C (2015) The effects of Uber’s surge pricing: A case study. Working paper, University of Chicago, Chicago.Google Scholar
  • Hao H, Padman R, Sun B, Telang R (2018) Quantifying the impact of social influence on the information technology implementation process by physicians: A hierarchical Bayesian learning approach. Inform. Systems Res. 29(1):25–41.LinkGoogle Scholar
  • Huang Y, Singh PV, Srinivasan K (2014) Crowdsourcing new product ideas under consumer learning. Management Sci. 60(9):2138–2159.LinkGoogle Scholar
  • Hunter T, Herring R, Abbeel P, Bayen A (2009) Path and travel time inference from GPS probe vehicle data. NIPS Analyzing Networks Learning Graphs (NIPS, Whistler, BC) 12(1):2.Google Scholar
  • Iyengar R, Ansari A, Gupta S (2007) A model of consumer learning for service quality and usage. J. Marketing Res. 44(4):529–544.CrossrefGoogle Scholar
  • Lee Y-J, Hosanagar K, Tan Y (2015) Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Sci. 61(9):2241–2258.LinkGoogle Scholar
  • Liao L, Patterson DJ, Fox D, Kautz H (2006) Building personal maps from GPS data. Ann. New York Acad. Sci. 1093(1):249–265.CrossrefGoogle Scholar
  • Liu D, Di Weng YL, Bao J, Zheng Y, Qu H, Wu Y (2017) SmartadP: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans. Visualization Comput. Graphics 23(1):1–10.CrossrefGoogle Scholar
  • Liu L, Andris C, Ratti C (2010a) Uncovering cabdrivers’ behavior patterns from their digital traces. Comput. Environ. Urban Systems 34(6):541–548.CrossrefGoogle Scholar
  • Liu S, Liu Y, Ni LM, Fan J, Li M (2010b) Toward mobility-based clustering. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM) (Association for Computing Machinery, New York, NY, USA), 919–928.Google Scholar
  • Liu S, Araujo M, Brunskill E, Rossetti R, Barros J, Krishnan R (2013) Understanding sequential decisions via inverse reinforcement learning. IEEE 14th Internat. Conf. Mobile Data Management (MDM), vol. 1 (IEEE, Piscataway, NJ), 177–186.Google Scholar
  • Liu Y, Whinston A (2019) Resolving Braess’s paradox through information design: Routing for heterogeneous autonomous vehicles. Inform. Econom. Policy 47:14–26.Google Scholar
  • Luo X, Andrews M, Fang Z, Phang CW (2013) Mobile targeting. Management Sci. 60(7):1738–1756.LinkGoogle Scholar
  • McFadden D (1974) Conditional logit analysis of qualitative choices. Zarembka P, ed. Frontiers in Econometrics (Academic Press, Cambridge, MA), 105--142.Google Scholar
  • Mehta N, Rajiv S, Srinivasan K (2004) Role of forgetting in memory-based choice decisions: A structural model. Quant. Marketing Econom. 2(2):107–140.CrossrefGoogle Scholar
  • Menon NM, Mishra A, Ye S (2020) Beyond related experience: Upstream vs. downstream experience in innovation contest platforms with interdependent problem domains. Manufacturing Service Oper. Management. Forthcoming.Google Scholar
  • Molitor D, Reichhart P, Spann M, Ghose A (2019) Measuring the effectiveness of location-based advertising: A randomized field experiment. Working paper. Fordham University, New York.Google Scholar
  • Okabe A, Boots B, Sugihara K, Chiu SN (2009) Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, vol. 501 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Phithakkitnukoon S, Veloso M, Bento C, Biderman A, Ratti C (2010) Taxi-aware map: Identifying and predicting vacant taxis in the city. International Joint Conference on Ambient Intelligence (pp. 86-95). Springer, Berlin, Heidelberg.Google Scholar
  • Reiss PC, Wolak FA (2007) Structural econometric modeling: Rationales and examples from industrial organization. Heckman JJ, Leamer E, eds. Handbook of Econometrics, vol. 6(A) (Elsevier),4277–4415.Google Scholar
  • Salz T, Lizzeri A, Frechette GR (2019) Frictions in a competitive, regulated market evidence from taxis. Amer. Econom. Rev. 109(8):2954–2992.Google Scholar
  • Shin S, Misra S, Horsky D (2012) Disentangling preferences and learning in brand choice models. Marketing Sci. 31(1):115–137.LinkGoogle Scholar
  • Tambe P (2014) Big data investment, skills, and firm value. Management Sci. 60(6):1452–1469.LinkGoogle Scholar
  • Tambe P, Hitt LM (2012) The productivity of information technology investments: New evidence from IT labor data. Inform. Systems Res. 23(3 pt. 1):599–617.Google Scholar
  • Wang Y, Wu C, Zhu T (2019) Mobile hailing technology and taxi driving behaviors. Marketing Sci. 8(5):734–755.Google Scholar
  • Yuan J, Zheng Y, Xie X, Sun G (2013) T-drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowledge Data Engrg. 25(1):220–232.CrossrefGoogle Scholar
  • Zhang S, Singh PV, Ghose A (2019) A structural analysis of the role of superstars in crowdsourcing contests. Inform. Systems Res. 30(1):15–33.LinkGoogle Scholar
  • Zhao Y, Yang S, Narayan V, Zhao Y (2013) Modeling consumer learning from online product reviews. Marketing Sci. 32(1):153–169.LinkGoogle Scholar
  • Zheng J, Tan Y, Ren F, Chen X (2020) Optimizing two-sided promotion for IS enabled transportation network: A conditional Bayesian learning model. Inform. Systems Res. Forthcoming.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.