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Extracting activity patterns from taxi trajectory data: A two-layer framework using spatio-temporal clustering, Bayesian probability, and Monte Carlo simulation

Research output: Contribution to journalArticle (Academic Journal)

Original languageEnglish
Number of pages25
JournalInternational Journal of Geographical Information Science
DateAccepted/In press - 6 Jul 2019
DatePublished (current) - 19 Jul 2019


Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference, and Monte Carlo simulation. Two million taxi trips in New York, the United States of 
America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.

    Research areas

  • spatio-temporal clustering, Bayesian probabilities, monte carlo simulation, travel behaviour analysis

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Taylor and Francis at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.53 MB, PDF document


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