In the past, the way that the discrete choice models treated the unobservable variables such as perceived and attitude variables caused some problems. For example, the estimated parameters were not consistent and efficient, the index could not be used directly to affect the policies, and the index may not be suitable in the prediction, and the unobservable variables are unchangeable as individuals vary. It results in some disadvantages for policies analysis and applications. Now the most widely used models is Maximum Simulated Likelihood to overcome the model with multi dimension parameter integration problems. There exists unstable situation in parameter calibrating as the number of simulated draws is not enough. The earlier reference seldom explored the influence of the model calibrating by simulating the number of draws. The number of draws is quite important. The purpose of this research is to use the simultaneous equations concepts for integrating latent variables and individual choice models, and decide the best number of the random draws. The parameters estimation will be more consistent, efficient, and stable. In addition, this study also discusses the heterogeneity, correlation, and hierarchy cause-and-effect of service quality perceived variables to increase the explanatory power in the models and to know the travelers’ riding behaviors. It will be implementing to policy analysis, to understand every policy variables on direct and indirect effect of choice behaviors, and to solve the problem which came about in the previous researches. The scope of this research was the intercity bus travelers of Taipei-Kaohsiung route in Taiwan. For the part of dealing with the latent variable choice models, the evidence shows that as the service satisfaction factors add in the model, the explanatory power will be improved. It also presented the methodology is efficient. For the part of the number of random draws, simulating the log-likelihood function of different latent variable choice models will boost as the simulating number increase. While the Halton random number reached to 4000, the improvement of the simulation to simulating the log-likelihood function was getting smooth. So this research picks 4,000 as the number of random draws. In this paper, if the latent variables are assumed as exogenous variables, the I II - - integrating latent variables heterogeneity and correlation of the choice model has better explanatory power. It presents that there are individual heterogeneity and correlation among the three service quality variables-convenience, commoditization and entertainment, the passengers’ interaction. In addition, there is positive influence on the travelers’ choices to the intercity bus companies by these three service quality satisfaction variables. The convenience and commoditization and entertainment satisfaction of the travelers to the bus companies will influence positively on travelers’ choice behaviors, and the convenience satisfaction is stronger than commoditization and entertainment satisfaction. Moreover, the travelers’ interaction satisfaction influence indirectly on the travelers’ choice behaviors to the bus companies through the convenience and commoditization and entertainment satisfaction of the travelers.
16Measurement of non-random attrition effects on mobility rates using trip diaries data
Measurement of non-random attrition effects on mobility rates using trip diaries data
29Frequent versus occasional drivers: A hybrid route choice model
Frequent versus occasional drivers: A hybrid route choice model
3A Comparison Between Multinomial Logit and Probit Models
Although the estimation of flexible multinomial discrete choice models generally needs the incorporation of simulation methods, their application is recently common in environmental and resource economics such as in many other economic disciplines (e.g. transportation economics). Based on a firm level data set of the German manufacturing sector, this paper examines determinants of environmental innovations by comparing the estimation results in flexible multinomial probit models and restrictive multinomial logit and independent probit models. The analysis of the two latter models implies that some specific environmental organizational measures, technological opportunities, and market pull factors have a significantly positive effect on both environmental product and process innovations. Taking this into consideration, the flexible multinomial probit model analysis provides few new insights since the simulated maximum likelihood estimations are rather unreliable as a consequence of the sole inclusion of firm-specific characteristics as explanatory variables. In this respect, the incorporation of simulation methods into the maximum likelihood estimations is not crucial since the problems do not decrease if the number of random draws in the considered Geweke-Hajivassiliou-Keane simulator rises. Furthermore, the difficulties grow if the number of choice alternatives increases. It can therefore be concluded that the applicability of these flexible multinomial discrete choice models without the incorporation of choice-specific attributes as explanatory variables is rather limited in practice.
Read more6A semi-compensatory choice model with probabilistic choice set: combining implicit choice set within probabilistic choice set formation
The standard random utility model assumes a fully rational and compensatory choice behaviour. However, various research studies have proven that non-compensatory /semi-compensatory choice behaviour is more realistic. This paper proposes a semi-compensatory framework for the discrete choice model that combines probabilistic choice set formation along with implicit choice constraints in choice making. It combines the Independent Availability Logit (IAL) with Implicit Constrained Multinomial Logit model (CMNL). The model is applied to investigate the mode choice behaviour of the Ottawa-Gatineau regions of Canada’s capital by using the household travel survey data. The explanatory power and elasticity measures of the proposed model are compared with IAL and MNL models. It is found that the IAL-CMNL model outperforms both IAL and MNL models and can reproduce the choice set formation process more effectively. The empirical investigation shows that the IAL-CMNL model results in relatively higher tolerance and softer constraints for cut-off violations compared to the IAL model. Elasticity calculations and outcomes of this research highlight the importance of capturing choice set formation and constrained choice behaviour in the choice modelling.
Read more8Integrated mixed logit and latent variable models
Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual’s decision-making process and therefore to yield more reliable part-worth estimates and market share predictions. Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. This paper starts from the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirical study presented here shows these ICLV models perform very well with respect to model fit and prediction.
Read more3Estimators for longitudinal latent exposure models: examining measurement model assumptions.
Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd.
Read more156Hybrid Choice Modeling of New Technologies for Car Choice in Canada
In the past decade, a new trend in discrete choice modeling has emerged: psychological factors are explicitly incorporated to enhance the behavioral representation of the choice process. In this context, hybrid models expand on standard choice models by including attitudes and perceptions as latent variables. The complete model is composed of a group of structural equations describing the latent variables in terms of observable exogenous variables and a group of measurement relationships linking latent variables to certain observable indicators. Although the estimation of hybrid models requires the evaluation of complex multidimensional integrals, simulated maximum likelihood is implemented to solve the integrated multiple-equation model. This study empirically evaluates the application of hybrid choice modeling to data from a survey conducted by the Energy and Materials Research Group (Simon Fraser University, 2002 and 2003) of the virtual personal vehicle choices made by Canadian consumers when they are faced with technological innovations. The survey also includes a complete list of indicators that allows the application of a hybrid choice model formulation. It is concluded that the hybrid choice model is genuinely capable of adapting to practical situations by including latent variables among the set of explanatory variables. The incorporation of perceptions and attitudes in this way leads to more realistic models and gives a better description of the profile of consumers and their adoption of new automobile technologies.
Read more19Planning and Action in a Model of Choice
Purpose: This chapter introduces a choice modeling framework that explicitly represents the planning and action stages of the choice process. Methodology: A discussion of evidence from behavioral research is followed by the development of a discrete choice modeling framework with explicit planning and action submodels. The plan/action choice model is formulated for both static and dynamic contexts; where the latter is based on the Hidden Markov Model. Plans are often unobservable and are treated as latent variables in model estimation using observed actions. Implications: By modeling the interactions between the planning and action stages, we are able to incorporate richer specifications in choice models with better predictive and policy analysis capabilities. The applications of this research in areas such as driving behavior, route choice, and mode choice demonstrate the advantages of the plan/action model in comparison to a “black box” choice model in terms of improved microsimulations of behaviors that better represent real-life situations. As such, the outcomes of this chapter are relevant to researchers and policy analysts.
Read more1Modeling factors to various drivers' actions due to vehicle malfunctions, the integrated latent class and latent variable model
It is known that how individuals perceive the situation result in choices. However, it is unknown, how drivers process the information of vehicle malfunctions based on the situation at hand. In this study, we only considered drivers' actions in cases where vehicles had some forms of malfunction at the time of the crash. Also due to endogeneity bias, or aggregation of the error term, we did not include the vehicle malfunction directly in our model. In addition, we used the latent variable (LV) for class allocation. Although the latent class model considering latent variable (LCLV) is not comparable with other considered models, by means of log likelihood, the results expanded our understanding regarding the indirect impact of an indicator of vehicle malfunction by means of latent variable on drivers' actions. Due to inclusion of latent variables in both, the indicator and the choice models, the relationship between those two models could be revealed based on the impacts of the latent variables. It was found that, for instance, the latent variable, which decreases the likelihood of driving vehicles with brake defect, at the same time increases taking driver action of speeding. Or female drivers are less likely to be involved in speeding driving actions before crashes due to various vehicles malfunctions. Driving under the influence of drug or alcohol or being under emotional conditions were some of the factors impacting various drivers actions. This is the earliest study implemented the technique in the context of traffic safety.
Read more17Models of individual preference and choice
The purpose of this paper is to provide an overview of models of individual preference and choice, and to direct the reader towards some of the vast literature on the topics that we shall consider. Our discussion will center around three basic approaches to the structure of preferences and choices, namely binary preferences, choice functions, and choice probability functions. These three approaches will be explored both separately and in terms of interconnections between them. Although an important aspect of decision making is the development and discovery of feasible decision alternatives, I shall not discuss this process explicitly. It will be presumed throughout that the individual is confronted with a non-empty set X of potential decision alternatives which will be denoted as x, y, a, b ..... Generally speaking, the alternafives in X are mutually incompatible in the sense that no more than one alternative can ultimately be chosen and implemented. Moreover, when discussing choices from specified subsets of X, it will generally be assumed that some alternative in each subset must be chosen when that subset is the feasible subset. This latter point deserves elaboration. To provide flexibility in discussing choices and relationships between choices from different 'environments' or subsets of X, we imagine that any one of a number of non-empty subsets of X might emerge as the set of alternatives actually available for implementation in the situation at hand. Notationally, M will denote this set of potentially feasible subsets of X, with members A, B,... Then we can speak about choices or potential choices from A, from B, and so forth, and consider relationships between these choices in view of relationships between the potential feasible subsets. For example, if A is a subset of B, or A _~ B, and if x ~ A is a 'choice' from B, then we
Read moreHeterogeneity and Strategic Choices: The Case of Stock Repurchases
Heterogeneity and Strategic Choices: The Case of Stock Repurchases
16Exploring the behavioral stage transition of traveler's adoption of carsharing: An integrated choice and latent variable model
Exploring the behavioral stage transition of traveler's adoption of carsharing: An integrated choice and latent variable model
Read moreTheoretical, methodological and practical issues of choice models
This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa.
 Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation
Read more1Factors Driving Battery and Solar Purchase Decision of Residents: a Behavioural Choice Experiment Using a Hybrid Discrete Choice and Latent Variable Model
In a mature rooftop solar PV market, home battery adoption is a household choice influenced by PV ownership and various factors. This study examines the determinants of solar PV and battery adoption in South East Queensland, Australia, using survey data and a hybrid discrete choice model. Four key psychological constructs shaping adoption decisions are identified: “adopter communicators,” “profit seekers,” “non‐adopter communicators,” and “socially‐conscious investors.” Separate models for current PV users and non‐adopters reveal distinct influences on adoption behaviour. Sociodemographic factors, including age, income, education, and employment status, significantly impact decisions. For PV owners, battery size is a critical factor in transitioning from PV‐only to a battery‐inclusive system. For non‐adopters, energy autonomy is a key motivation for purchasing an integrated PV‐battery system. The findings suggest that effective policy interventions must be tailored to each group, combining direct financial incentives and indirect regulatory measures to drive battery adoption. Understanding these behavioural and demographic drivers can help policymakers design targeted strategies to enhance the penetration of battery storage systems, supporting a more sustainable and resilient energy future.
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4Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data
Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.
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