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Modelling of intermodal systems
Published in Jason Monios, Rickard Bergqvist, Intermodal Freight Transport and Logistics, 2017
Jonas Flodén, Dries Meers, Cathy Macharis
A multinomial logit model makes a choice between a number of equal options. However, sometimes a more realistic representation of the modal choice is to make the choice in steps, for example a traveller might make a choice between using a car, bike or public transportation, but the type of public transport used is only selected as a second step after the decision to use public transport has been made. A logit model that allows for this is called a nested logit model, where choices with the same attributes are put in a ‘nest’ (see Figure 11.8, where the logit models are depicted as a tree structure).
Speed management in single carriageway roads: Speed limit setting through expert-based system
Published in Gianluca Dell’Acqua, Fred Wegman, Transport Infrastructure and Systems, 2017
N. Gregório, A. Bastos Silva, A. Seco
The hypotheses assumed about the statistical distribution of the error term of the utility function lead to the adoption of different types of discrete choice models. The Logit models are one of them. The Multinomial Logit model, which is part of this family, was developed as a generalization of a binary choice model in a context involving more than two alternatives.
Senior Migration and Housing Consumption Findings from Metropolitan Baltimore
Published in Journal of Aging and Environment, 2021
A commonly known problem with a multinomial logit model is the potential violation of the independence of irrelevant alternatives (IIA) assumption (McFadden, 1977). Although many scholars typically rely on structuring a nested logit model (Kim et al., 2005; Lee & Waddell, 2010) and a mixed logit or probit model (Détang‐Dessendre et al., 2008; Marois et al., 2019) for solutions, the absence of alternative specific variables allows us to use multinomial logit specification without biased estimations. Bonnet et al. (2010) argue the multinomial logistic specification is preferred over a nested logit in cases where there is no alternative specific variable. Focusing on the comparison of movers to a specific subarea and stayers, migration decisions and housing consumption for relocation to the central city, inner suburbs, and outer suburbs are examined separately at the household level.
Express analysis for prioritization: Best–Worst Scaling alteration to System 1
Published in Journal of Management Analytics, 2020
Choice probability among the alternatives can be defined by the multinomial-logit (MNL) modelwhere xij are the predictor indicator variables (1 and 0 – the jth variable is presented or not in the ith task, respectively, and N is a number of all responses), and aj are the utility parameters for estimation probability pij of each jth choice among all n of them in an ith task. As it was shown in Lipovetsky (2015); Lipovetsky and Conklin (2014a), it is possible to build MNL parameters via binary logit modeling, and even analytical closed form solution can be obtained for dichotomic or categorical predictors.
Determinants of household energy choice in West Shoa Zone: in the case of Ambo Town
Published in International Journal of Green Energy, 2022
Takele Abdisa Nikus, Boharsa Garoma Wayessa
The logit does not require numerical integration and almost always converges to a global optimum. However, in the case of choice models, the normal distribution assumption for error terms leads to the Multinomial logit Model (MNM) which has some properties that make it difficult to use in choice analysis more than two alternatives. The mathematical structure of the Multinomial Logit Model is given as choice probabilities of each alternative as a function of the systematic portion of the utility of all the alternatives. The general expression for the probability of choosing an alternative i (i = 1, 2 … J) from a set of j alternatives are: