Advertisement

Modeling Social Information Learning among Taxi Drivers

  • Siyuan Liu
  • Ramayya Krishnan
  • Emma Brunskill
  • Lionel M. Ni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

When a taxi driver of an unoccupied taxi is seeking passengers on a road unknown to him or her in a large city, what should the driver do? Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows (e.g., hotel taxi rank)? This is an interesting problem that arises everyday in many cities worldwide. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him (as in he has not picked up or dropped off passengers there before). Our observation from large scale taxi drivers behavior data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as Socialized Information Learning (SIL), second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.

Keywords

Socialize Knowledge Experienced Knowledge Baseline Method Transfer Learning Uncertain Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alpern, S., Reyniers, D.: Spatial dispersion as a dynamic coordination problem. Theory and Decision 53(1) (2002)Google Scholar
  2. 2.
    Argote, L., Miron-Spektor, E.: Organizational learning: From experience to knowledge. Organization Science 22 (2011)Google Scholar
  3. 3.
    Argyris, C., Schn, D.: Organizational learning: A theory of action perspective. Addison-Wesley (1978)Google Scholar
  4. 4.
    Bhatt, G.: Information dynamics, learning and knowledge creation in organizations. The Learning Organization 7(2) (2000)Google Scholar
  5. 5.
    Bontis, N., Coss, V.M.: Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies 39 (2002)Google Scholar
  6. 6.
    Cao, B., Pan, J., Zhang, Y., Yeung, D., Yang, Q.: Adaptive transfer learning. In: Proc. of AAAI (2010)Google Scholar
  7. 7.
    Deng, K., Pineau, J., Murphy, S.: Active learning for developing personalized treatment. In: Proc. of UAI (2011)Google Scholar
  8. 8.
    Devlin, K.: A framework for modeling evidence-based, context-influenced reasoning. In: Proc. of CONTEXT (2003)Google Scholar
  9. 9.
    Forbes, J., Huang, T., Kanazawa, K., Russell, S.: The batmobile: Towards a bayesian automated taxi. In: Proc. of IJCAI (1995)Google Scholar
  10. 10.
    Fu, W., Song, L., Xing, E.P.: Dynamic mixed membership blockmodel for evolving networks. In: Proc. of ICML (2009)Google Scholar
  11. 11.
    Ge, Y., Xiong, H., Liu, C., Zhou, Z.-H.: A taxi driving fraud detection system. In: Proc. of ICDM (2011)Google Scholar
  12. 12.
    Glaubius, R., Tidwell, T., Gill, C., Smart, W.: Real-time scheduling via reinforcement learning. In: Proc. of UAI (2010)Google Scholar
  13. 13.
    Heylighen, F.: Collective intelligence and its implementation on the web: Algorithms to develop a collective mental map. Comput. Math. Organ. Theory 5(3), 253–280 (1999)zbMATHCrossRefGoogle Scholar
  14. 14.
    Kay, J., Niu, W.T., Carmichael, D.J.: Oncor: ontology- and evidence-based context reasoner. In: Proc. of IUI (2007)Google Scholar
  15. 15.
    Lee, S.J., Popović, Z.: Learning behavior styles with inverse reinforcement learning. ACM Trans. Graph. 29 (July 2010)Google Scholar
  16. 16.
    Li, P., Yu, J.X., Liu, H., He, J., Du, X.: Ranking individuals and groups by influence propagation. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 407–419. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Liu, S., Liu, C., Luo, Q., Ni, L., Krishnan, R.: Calibrating large scale vehicle trajectory data. In: Proc. of IEEE MDM (2012)Google Scholar
  18. 18.
    Liu, S., Liu, Y., Ni, L.M., Fan, J., Li, M.: Towards mobility-based clustering. In: Proc. of ACM SIGKDD (2010)Google Scholar
  19. 19.
    Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proc. of ICML (2011)Google Scholar
  20. 20.
    Rettinger, A., Nickles, M., Tresp, V.: Statistical relational learning of trust. Mach. Learn. 82(2) (2011)Google Scholar
  21. 21.
    Ronald, N., Dignum, V., Jonker, C., Arentze, T., Timmermans, H.: On the engineering of agent-based simulations of social activities with social networks. Inf. Softw. Technol. 54(6) (2012)Google Scholar
  22. 22.
    Russell, S.: Learning agents for uncertain environments (extended abstract). In: Proc. of COLT (1998)Google Scholar
  23. 23.
    Szuba, T., Polański, P., Schab, P., Wielicki, P.: On Efficiency of Collective Intelligence Phenomena. In: Nguyen, N.T., Kowalczyk, R. (eds.) Transactions on CCI III. LNCS, vol. 6560, pp. 50–73. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Templeton, G.F., Lewis, B.R., Snyder, C.A.: Development of a measure for the organizational learning construct. J. Manage. Inf. Syst. 19(2) (2002)Google Scholar
  25. 25.
    Wang, H., Yang, Q.: Transfer learning by structural analogy. In: Proc. of AAAI (2011)Google Scholar
  26. 26.
    Xiao, L., Zhou, D., Wu, M.: Hierarchical classification via orthogonal transfer. In: Proc. of ICML (2011)Google Scholar
  27. 27.
    Zhu, X., Gibson, B., Rogers, T.: Co-training as a human collaboration policy. In: Proc. of AAAI (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Siyuan Liu
    • 1
  • Ramayya Krishnan
    • 1
  • Emma Brunskill
    • 1
  • Lionel M. Ni
    • 2
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.Hong Kong University of Science and TechnologyHong Kong

Personalised recommendations