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From taxi GPS traces to social and community dynamics: A survey

Castro, Pablo Samuel, Zhang, Daqing, Chen, Chao, Li, Shijian, Pan, Gang·2013·ACM Computing Surveys

Abstract

Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.

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Metadata

Authors
Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G.
Venue
ACM Computing Surveys
Published
2013-11
Volume
46
Issue
2
Pages
1-34
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
OpenAlex
W1995151908
Cited by
252
References
3
FWCI
51.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: From taxi GPS traces to social and community dynamics: A survey. https://app.cochat.ai/papers/10.1145/2543581.2543584
The paper
Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G. (2013). From taxi GPS traces to social and community dynamics: A survey. ACM Computing Surveys. https://doi.org/10.1145/2543581.2543584

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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