Integrated data imputation and annotation tool: trace annotator

T. Feng; H.J.P. Timmermans

doi:10.3929/ethz-b-000225623

Integrated data imputation and annotation tool: trace annotator

T. Feng, H.J.P. Timmermans

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

This paper presents a tool for integrated imputation and annotation of GPS tracking data, Trace Annotator. The tool is designed to efficiently and effectively transform raw GPS data into segmented activity and travel data where transportation modes, travel purpose at destinations, time and route information related to the spatial and temporal profiles were automatically generated. Bayesian belief network and other main machine learning approaches have been incorporated. The tool has been empirically applied and validated in several national and international pilots

Original language	English
Title of host publication	Adjunct Proceedings of the 14th International Conference on Location Based Services
Editors	Peter Kiefer, Haosheng Huang, Nico van de Weghe, Martin Raubal
Place of Publication	Zurich
Publisher	ETH Zürich
Pages	221-222
Number of pages	2
DOIs	https://doi.org/10.3929/ethz-b-000225623
Publication status	Published - 2018
Event	14th Conference on Location Based Services - Zurich, Switzerland Duration: 15 Jan 2018 → 17 Jan 2018 http://lbs18.ethz.ch/

Conference

Conference	14th Conference on Location Based Services
Abbreviated title	LBS 2018
Country/Territory	Switzerland
City	Zurich
Period	15/01/18 → 17/01/18
Internet address	http://lbs18.ethz.ch/

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@inproceedings{3abd800c83364c5188ae99702711bfa9,

title = "Integrated data imputation and annotation tool: trace annotator",

abstract = "This paper presents a tool for integrated imputation and annotation of GPS tracking data, Trace Annotator. The tool is designed to efficiently and effectively transform raw GPS data into segmented activity and travel data where transportation modes, travel purpose at destinations, time and route information related to the spatial and temporal profiles were automatically generated. Bayesian belief network and other main machine learning approaches have been incorporated. The tool has been empirically applied and validated in several national and international pilots",

author = "T. Feng and H.J.P. Timmermans",

year = "2018",

doi = "10.3929/ethz-b-000225623",

language = "English",

pages = "221--222",

editor = "Peter Kiefer and Haosheng Huang and \{van de Weghe\}, Nico and Martin Raubal",

booktitle = "Adjunct Proceedings of the 14th International Conference on Location Based Services",

publisher = "ETH Z{\"u}rich",

note = "14th Conference on Location Based Services ; Conference date: 15-01-2018 Through 17-01-2018",

url = "http://lbs18.ethz.ch/",

}

Feng, T & Timmermans, HJP 2018, Integrated data imputation and annotation tool: trace annotator. in P Kiefer, H Huang, N van de Weghe & M Raubal (eds), Adjunct Proceedings of the 14th International Conference on Location Based Services. ETH Zürich, Zurich, pp. 221-222, 14th Conference on Location Based Services, Zurich, Switzerland, 15/01/18. https://doi.org/10.3929/ethz-b-000225623

Integrated data imputation and annotation tool: trace annotator. / Feng, T.; Timmermans, H.J.P.
Adjunct Proceedings of the 14th International Conference on Location Based Services. ed. / Peter Kiefer; Haosheng Huang; Nico van de Weghe; Martin Raubal. Zurich: ETH Zürich, 2018. p. 221-222.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Feng, T.

AU - Timmermans, H.J.P.

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N2 - This paper presents a tool for integrated imputation and annotation of GPS tracking data, Trace Annotator. The tool is designed to efficiently and effectively transform raw GPS data into segmented activity and travel data where transportation modes, travel purpose at destinations, time and route information related to the spatial and temporal profiles were automatically generated. Bayesian belief network and other main machine learning approaches have been incorporated. The tool has been empirically applied and validated in several national and international pilots

AB - This paper presents a tool for integrated imputation and annotation of GPS tracking data, Trace Annotator. The tool is designed to efficiently and effectively transform raw GPS data into segmented activity and travel data where transportation modes, travel purpose at destinations, time and route information related to the spatial and temporal profiles were automatically generated. Bayesian belief network and other main machine learning approaches have been incorporated. The tool has been empirically applied and validated in several national and international pilots

U2 - 10.3929/ethz-b-000225623

DO - 10.3929/ethz-b-000225623

M3 - Conference contribution

SP - 221

EP - 222

BT - Adjunct Proceedings of the 14th International Conference on Location Based Services

A2 - Kiefer, Peter

A2 - Huang, Haosheng

A2 - van de Weghe, Nico

A2 - Raubal, Martin

PB - ETH Zürich

CY - Zurich

Y2 - 15 January 2018 through 17 January 2018

ER -

Integrated data imputation and annotation tool: trace annotator

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