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http://hdl.handle.net/1942/24099
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DC Field | Value | Language |
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dc.contributor.author | ECTORS, Wim | - |
dc.contributor.author | REUMERS, Sofie | - |
dc.contributor.author | LEE, Won Do | - |
dc.contributor.author | Choi, Keechoo | - |
dc.contributor.author | KOCHAN, Bruno | - |
dc.contributor.author | JANSSENS, Davy | - |
dc.contributor.author | BELLEMANS, Tom | - |
dc.contributor.author | WETS, Geert | - |
dc.date.accessioned | 2017-08-07T08:45:16Z | - |
dc.date.available | 2017-08-07T08:45:16Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Transportmetrica A-Transport Science, 13(8), p. 742-766 | - |
dc.identifier.issn | 2324-9935 | - |
dc.identifier.uri | http://hdl.handle.net/1942/24099 | - |
dc.description.abstract | The generation of substantial amounts of travel- and mobility-related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation/annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area. | - |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea funded by the Korean government (MSIP) under grant NRF-2010-0028693. | - |
dc.language.iso | en | - |
dc.rights | © 2017 Hong Kong Society for Transportation Studies Limited | - |
dc.subject.other | activity classification; activity class optimisation; big data annotation; trip purpose imputation; classification algorithms; activity entropy | - |
dc.title | Developing an optimised activity type annotation method based on classification accuracy and entropy indices | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 766 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 742 | - |
dc.identifier.volume | 13 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Lee, WD (reprint author), Manchester Metropolitan Univ, Crime & Well Being Big Data Ctr, Manchester M15 6BH, Lancs, England. w.lee@mmu.ac.uk | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1080/23249935.2017.1331275 | - |
dc.identifier.isi | 000404923400004 | - |
item.contributor | ECTORS, Wim | - |
item.contributor | REUMERS, Sofie | - |
item.contributor | LEE, Won Do | - |
item.contributor | Choi, Keechoo | - |
item.contributor | KOCHAN, Bruno | - |
item.contributor | JANSSENS, Davy | - |
item.contributor | BELLEMANS, Tom | - |
item.contributor | WETS, Geert | - |
item.fullcitation | ECTORS, Wim; REUMERS, Sofie; LEE, Won Do; Choi, Keechoo; KOCHAN, Bruno; JANSSENS, Davy; BELLEMANS, Tom & WETS, Geert (2017) Developing an optimised activity type annotation method based on classification accuracy and entropy indices. In: Transportmetrica A-Transport Science, 13(8), p. 742-766. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2018 | - |
crisitem.journal.issn | 2324-9935 | - |
crisitem.journal.eissn | 2324-9943 | - |
Appears in Collections: | Research publications |
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bbbb.pdf Restricted Access | Published version | 3.66 MB | Adobe PDF | View/Open Request a copy |
aaa.1.pdf | Peer-reviewed author version | 960.24 kB | Adobe PDF | View/Open |
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