Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

4-2013

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.

Discipline

Theory and Algorithms

Publication

Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II

Volume

7819

First Page

73

Last Page

84

ISBN

978-3-642-37456-2

Identifier

10.1007/978-3-642-37456-2_7

Publisher

Springer

Copyright Owner and License

LARC

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