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http://hdl.handle.net/1942/7965
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DC Field | Value | Language |
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dc.contributor.author | MOONS, Elke | - |
dc.contributor.author | WETS, Geert | - |
dc.contributor.author | AERTS, Marc | - |
dc.date.accessioned | 2008-03-13T10:08:25Z | - |
dc.date.available | 2008-03-13T10:08:25Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS. p. 183-194 | - |
dc.identifier.isbn | 978-3-540-77000-8 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/1942/7965 | - |
dc.description.abstract | Due to the increasing complexity in transportation systems, one needs to search for different ways to model the separate components of these systems. A general transportation system comprises components/models concerning mode choice, travel duration, trip distance, departure time, accompanying individuals, etc. This paper tries to discover whether semi- and nonlinear models bring an added value to transportation analysis in general and mode choice modelling in particular. Linear (logistic regression), semi-linear (multiple fractional polynomials) and nonlinear (support vector machines and classification and regression trees) models are applied to several binary settings and compared to each other based on sensitivity (i.e. the proportion of positive cases that are predicted correctly). In general, one can state that on skewed data sets, linear and semi-linear models tend to perform better, whereas on more balanced data sets both nonlinear models yield better results. Future research will take a closer look at other extensions of the well-established linear regression model. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science | - |
dc.title | Nonlinear models for determining mode choice - (Accuracy is not always the optimal goal) | - |
dc.type | Journal Contribution | - |
local.bibliographicCitation.authors | Neves, J. | - |
local.bibliographicCitation.authors | Santos, MF | - |
local.bibliographicCitation.authors | Machado, JM | - |
local.bibliographicCitation.conferencedate | DEC 03-07, 2007 | - |
local.bibliographicCitation.conferencename | Portuguese Conference on Artificial Intelligence | - |
dc.bibliographicCitation.conferencenr | 13 | - |
local.bibliographicCitation.conferenceplace | Guimaraes, PORTUGAL | - |
dc.identifier.epage | 194 | - |
dc.identifier.spage | 183 | - |
local.format.pages | 12 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Hasselt Univ, Transportat Res Inst, Diepenbeek, B-3590 Belgium.Moons, E, Hasselt Univ, Transportat Res Inst, Sci Pk 5-6, Diepenbeek, B-3590 Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.relation.ispartofseriesnr | 4874 | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1007/978-3-540-77002-2_16 | - |
dc.identifier.isi | 000252074800016 | - |
item.contributor | MOONS, Elke | - |
item.contributor | WETS, Geert | - |
item.contributor | AERTS, Marc | - |
item.validation | ecoom 2009 | - |
item.fulltext | No Fulltext | - |
item.accessRights | Closed Access | - |
item.fullcitation | MOONS, Elke; WETS, Geert & AERTS, Marc (2007) Nonlinear models for determining mode choice - (Accuracy is not always the optimal goal). In: PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS. p. 183-194. | - |
crisitem.journal.issn | 0302-9743 | - |
Appears in Collections: | Research publications |
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