Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/26621
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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 | KOCHAN, Bruno | - |
dc.contributor.author | JANSSENS, Davy | - |
dc.contributor.author | BELLEMANS, Tom | - |
dc.contributor.author | WETS, Geert | - |
dc.date.accessioned | 2018-08-07T07:28:19Z | - |
dc.date.available | 2018-08-07T07:28:19Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Future generation computer systems, 110, p. 338-349. | - |
dc.identifier.issn | 0167-739X | - |
dc.identifier.uri | http://hdl.handle.net/1942/26621 | - |
dc.description.abstract | Despite the advantages, big transport data are characterized by a considerable disadvantage as well. Personal and activity-travel information are often lacking, making it necessary to deduce this information with data mining techniques. However, some studies predict many unique activity type classes (ATCs), while others merge multiple activity types into larger ATCs. This action enhances the activity inference estimation, but destroys important activity information. Previous studies do not provide a strong justification for this practice. An objectively optimized set of ATCs, balancing model prediction accuracy and preserving activity information from the original data, becomes essential. Previous research developed a classification methodology in which the optimal set of ATCs was identified by analyzing all possible ATC combinations. However, this approach is practically impossible in a finite amount of time for e.g. the US National Household Travel Survey (NHTS) 2009 data set, which comprises 36 ATCs (home activity excluded), since there would be 3.82 · 1030 unique combinations (an exponential increase). The aim of this paper is to optimize which original ATCs should be grouped into a new class, and this for data sets for which it is impossible or impractical to simply calculate all ATC combinations. The proposed method defines anoptimization parameter U (based on classification accuracy and information retention) which is maximized in an iterative local search algorithm. The optimal set of ATCs for the NHTS 2009 data set was determined. A comparison finds that this optimum is considerably better than many expert opinion activity type classification systems. Convergence was confirmed and large performance gains were found | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.rights | 2018 Elsevier B.V. All rights reserved. | - |
dc.subject.other | Activity type classification | - |
dc.subject.other | (Big) Transport data annotation | - |
dc.subject.other | Optimal set of activity types | - |
dc.subject.other | Local search algorithm | - |
dc.subject.other | Classification accuracy | - |
dc.subject.other | Entropy indices | - |
dc.title | Optimizing copious activity type classes based on classification accuracy and entropy retention | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 349 | - |
dc.identifier.spage | 338 | - |
dc.identifier.volume | 110 | - |
local.format.pages | 12 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.class | dsPublValOverrule/author_version_not_expected | - |
dc.identifier.doi | 10.1016/j.future.2018.04.080 | - |
dc.identifier.isi | 000541153400031 | - |
dc.identifier.eissn | 1872-7115 | - |
local.provider.type | Web of Science | - |
local.uhasselt.international | yes | - |
item.contributor | ECTORS, Wim | - |
item.contributor | REUMERS, Sofie | - |
item.contributor | LEE, Won Do | - |
item.contributor | KOCHAN, Bruno | - |
item.contributor | JANSSENS, Davy | - |
item.contributor | BELLEMANS, Tom | - |
item.contributor | WETS, Geert | - |
item.validation | ecoom 2022 | - |
item.fullcitation | ECTORS, Wim; REUMERS, Sofie; LEE, Won Do; KOCHAN, Bruno; JANSSENS, Davy; BELLEMANS, Tom & WETS, Geert (2020) Optimizing copious activity type classes based on classification accuracy and entropy retention. In: Future generation computer systems, 110, p. 338-349.. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0167-739X | - |
crisitem.journal.eissn | 1872-7115 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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paper.pdf | Non Peer-reviewed author version | 499.09 kB | Adobe PDF | View/Open |
main.pdf Restricted Access | Published version | 1.15 MB | Adobe PDF | View/Open Request a copy |
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