000175102 001__ 175102
000175102 005__ 20181203022638.0
000175102 0247_ $$2doi$$a10.3141/2175-11
000175102 022__ $$a0361-1981
000175102 02470 $$2ISI$$a000286448700011
000175102 037__ $$aARTICLE
000175102 245__ $$aAnalysis of Implicit Choice Set Generation Using a Constrained Multinomial Logit Model
000175102 260__ $$bNational Academy of Sciences$$c2010
000175102 269__ $$a2010
000175102 336__ $$aJournal Articles
000175102 520__ $$aDiscrete choice models are defined conditional to the analyst's knowledge of the actual choice set. The common practice for many years has been to assume that individual-based choice sets can be deterministically generated on the basis of the choice context and characteristics of the decision maker. This assumption is not valid or not applicable in many situations, and probabilistic choice set formation procedures must be considered. The constrained multinomial logit model (CMNL) has recently been proposed as a convenient way to deal with this issue, as it is also appropriate for models with a large choice set. In this paper, how well the implicit choice set generation of the CMNL approximates the explicit choice set generation is analyzed as described in earlier research. The results based on synthetic data show that the implicit choice set generation model may be a poor approximation of the explicit model.
000175102 6531_ $$aRandom Utility-Models
000175102 700__ $$0240563$$aBierlaire, Michel$$g118332
000175102 700__ $$0243036$$aHurtubia, Ricardo$$g184510
000175102 700__ $$0243042$$aFloetteroed, Gunnar$$g188382
000175102 773__ $$j2175$$q92-97$$tTransportation Research Record
000175102 909C0 $$0252123$$pTRANSP-OR$$xU11418
000175102 909CO $$ooai:infoscience.tind.io:175102$$particle$$pENAC
000175102 917Z8 $$x118332
000175102 937__ $$aEPFL-ARTICLE-175102
000175102 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000175102 980__ $$aARTICLE