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Understanding taxi drivers’ routing choices from spatial and social traces

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Abstract

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road 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 cities all over the world. 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 he has not picked up or dropped off passengers there before). Our observation from large scale taxi driver trace 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.

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Siyuan Liu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Siyuan Liu

  3. Heinz College, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Siyuan Liu & Ramayya Krishnan

  4. Key Lab of Intellectual Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shuhui Wang

  5. School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Ce Liu

Authors
  1. Siyuan Liu
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  2. Shuhui Wang
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  3. Ce Liu
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  4. Ramayya Krishnan
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Additional information

Siyuan Liu is a research scientist at Carnegie Mellon University. He received his first PhD degree from Department of Computer Science and Engineering at Hong Kong University of Science and Technology, China in 2011, and the second PhD degree from University of Chinese Academy of Sciences in 2004. His research interests include big mobile data management and heterogeneous social networks mining.

Shuhui Wang received the PhD degree from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China in 2012. He is currently an assistant professor with the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. His research interests include largescale Web data mining, visual semantic analysis and machine learning.

Ce Liu is a PhD student at telecommunications and networking in School of Information Sciences of University of Pittsburgh. Her research interests include vehicular networking and wireless networks.

Ramayya Krishnan is the dean of Heinz College. He holds the John Heinz III Deanship and is the W. W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Carnegie Mellon University. He has a PhD degree in Management Science and Information Systems from the University of Texas at Austin.

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Liu, S., Wang, S., Liu, C. et al. Understanding taxi drivers’ routing choices from spatial and social traces. Front. Comput. Sci. 9, 200–209 (2015). https://doi.org/10.1007/s11704-014-4177-4

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  • DOI: https://doi.org/10.1007/s11704-014-4177-4

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