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Spatial Dependence in LUTI Models
Published in Rubén Cordera, Ángel Ibeas, Luigi dell’Olio, Borja Alonso, Land Use–Transport Interaction Models, 2017
Rubén Cordera, Luigi dell’Olio
Both spatial regression models and discrete choice models considering correlation between the errors of the alternatives are normally estimated using maximum likelihood. Specialised spatial regression software can be used for this; examples are Geoda (Anselin and Rey 2014), the spdep package for the R programming language (Bivand et al. 2015) and, in the field of discrete choice models, software like NLOGIT (Greene 2012) or Biogeme (Bierlaire 2003). Spatial regression models can also be estimated using alternative methods such as spatial two-stage least squares or the method of moments estimator.
Test
Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
Stated preference discrete choice experiments can be designed using JMP from the SAS Institute Inc. This software has a powerful platform for choice designs as well as a good platform for estimating choice models. Nlogit, an extension of the econometric software Limdep, is the gold standard in choice modeling. This package has all the latest developments in the choice analysis area as it should since its developer is a leading researcher in choice analysis.8 Stata will also handle choice estimation. R has packages for estimation but they are a challenge to use.
Modeling dynamic distribution of dilemma zone at signalized intersections for developing world traffic
Published in Journal of Transportation Safety & Security, 2022
Digvijay S. Pawar, Dibyendu Pathak, Gopal R. Patil
Binary discrete choice models are well suited for describing and testing the hypothesis about the relationship between a binary response outcome and one or more explanatory variables that can be categorical or quantitative. In the present case, drivers approaching intersection during yellow interval have to decide whether to stop or pass. Binary discrete choice models are developed to determine the probability of stopping for a given distance and speed of the approaching vehicle during the yellow interval. The probability of stopping for a given distance/speed Y is given by Equation 1. where, are the explanatory variables that influence the behavior of drivers and are the corresponding coefficients. The responses of drivers (stopping or passing through) during yellow onset were coded as one or zero. The logistic regression analysis was carried out in NLOGIT 4.0 software.
Willingness to pay for the outcomes of improved stormwater management
Published in Urban Water Journal, 2022
Robert Gillespie, Jeff Bennett
Models for the choice data collected from each of the samples were estimated using NLOGIT 4.0 (Econometric Software 2007). The explanatory variables used in the choice models are shown in Table 3 and include the attributes used to describe outcomes from across catchment stormwater actions, an ASC to account for systematic unobserved components that influence respondent’s choices, and socio-demographic variables. Socio-demographic variables were introduced into all models as interactions with the ASC to avoid singularity of the matrix.
Public transportation quality of service: factors, models, and applications
Published in Transport Reviews, 2019
Sajad Askari, Farideddin Peiravian
Chapter 9 describes and compares the three most commonly used data mining approaches for PT quality modelling, namely Artificial Neural Networks (ANN), Bayesian Networks (BN), and Decision Trees (DT). The concept of desired quality is the subject of Chapter 10, which proposes a methodology to use stated preferences surveys for its determination. It also presents the estimation of logit discrete choice models in order to model and analyse the results, an example of which is demonstrated using the NLogit software.