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)


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.


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.


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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

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