Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models

The first attempt to improve battery performance through model-based design optimization was made by W. Tiedemann and J. Newman in 19751. They varied the electrode thickness and porosity and then observed the effects on the effective capacity. Newman later applied the reaction-zone model to maximize the specific energy of the system using the same varying and observing method2. V. Ramadesigan et al.3developed optimal spatially varying porosity for porous electrodes by considering ohmic resistance model in the electrode. They minimized the ohmic resistance in the positive electrode by varying porosity using control vector parameterization (CVP) method. They also introduced the idea of using graded electrode to further improve the performace. Since the introduction of the pseudo-2D (P2D) model by the Newman group, many efforts have been made in utilizing the P2D model in battery design. W. Du et al. applied an optimization framework based on surrogate method4. S. Golmon et al. performed the multi-objective and multi-design-parameter optimization problem with adjoint sensitivity analysis5 incorporating mechanical stress-strain relationship. S. De and his coworkers6 used a reformulated model, to perform simultaneous optimization of multiple design parameters. N. Xue et al. 7 applied the gradient-based algorithm framework to optimize the cell design. More recently, Y. Dai and V. Srinivasan8used a gradient-free direct search method to maximize the specific energy. Z. Du et al. studies the effects of various design parameters on thick electrode performance9. Most of the aforementioned optimization research, used the sequential optimization approach, also known as CVP. CVP takes the differential algebraic equation (DAE) system describing the battery physics, applies a certain nonlinear programming (NLP) solver to them, discretizes only the control variables, and solves the model. The only exception being the effort by Golmon et al which used an indirect approach. Note that as of today, global optimization cannot be guaranteed when CVP type approaches are used with P2D type models. In general optimization approaches can be classified as indirect and direct approach. In the direct approach there are two main categories – sequential and simultaneous approach. The simultaneous approach10, on the other hand, discretizes both the control variables and the design variables before solving the problem. When used for optimization, the DAE system will only be solved once at the optimal point, compared to the repeated numerical integration needed for sequential optimization. By using high order discretization scheme on both the state and the design variables, simultaneous optimization approach results in faster determination of the optimum with fewer time steps. Furthermore, this approach offers more flexibility over constraints on the state variables. Since the state variables are also discretized, it is possible to apply equality/inequality constraints on their internal values. In this work, we will first apply the simultaneous optimization approach to a resistance model similar to the one mentioned earlier3, and compare its performance with the sequential approach. We will also explore the application of the same optimization approach to the P2D model, and identify the challenges. It is viewed as a controversial topic in the literature wherein different authors disagree on the relevance of graded porosity for improved performance8. By using a simultaneous optimization approach and guaranteeing global optimization for convex problem statements, we hope to provide additional insights. Acknowledgements The authors are thankful for the financial support by the Clean Energy Institute at the University of Washington and the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U.S. Department of Energy through the Advanced Battery Materials Research (BMR) Program (Battery500 Consortium).

BackgroundEndovascular thrombectomy (EVT) is the standard of care for patients with acute ischemic stroke (AIS) and intracranial vessel occlusion. Tandem occlusions (TO) comprise 20% of all anterior circulation AIS and...

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.

The present work proposes a methodology for optimization of a liquefied biomethane (LBM) production plant. The LBM production plant comprises amine-based absorption upgrading followed by a single expander refrigeration cycle. The processes were modeled using Aspen HYSYS® and optimized through a Sequential Quadratic Programming algorithm. Any changes in the operating conditions of the upgrading process will affect the cooling demand in the liquefaction, while the opposite is not true. Based on this, a sequential optimization approach starting with the upgrading process is proposed. In order to accommodate the connection between the processes, different objective functions were formulated for the sequential optimization approach. The results from the sequential approach were compared with an overall optimization approach, where the entire LBM plant was optimized simultaneously. The results indicate that the same solution was obtained both for the sequential approach and the simultaneous approach. For the sequential approach, however, the best result was observed when the interaction between the upgrading and liquefaction processes was accounted for by considering the effect of the upgrading process on the exergy requirement in the liquefaction process.

Frequent versus occasional drivers: A hybrid route choice model

Measurement of non-random attrition effects on mobility rates using trip diaries data

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.

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.

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.

As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates a broad range of travel demand management (TDM) policies, both in terms of passenger flow control and dynamic pricing strategies. This work aims to reveal the impact of TDM on metro commuters’ behavioural loyalty using stated-preference data collected in Guangzhou, China. Commuters’ behavioural response to TDM strategies is investigated in terms of the possible shift in departure time and travel mode. A hybrid choice model framework is used to incorporate four latent variables of interest, i.e., service quality, overall impression, external attractiveness and switching cost, into the discrete choice model and thereby capture the relationships between the attitudinal factors and observed variables. The model estimation results indicate that the four latent variables all prove useful in interpreting commuters’ behavioural loyalty. Commuters’ perceived service quality and overall impression both show a positive effect on their willingness to continue travelling by metro and are thus instructive for ridership retention. External attractiveness is found to be significant only in the case of the tendency to shift to a private car. Switching costs reveal commuters’ emotional attachment to their already developed commuting habit. These insights into commuters’ behavioural change intention enable metro operators to enhance commuters’ loyalty to their service and develop more effective TDM strategies in future practice.

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

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.

Prelims

Hybrid Choice Models: Principles and Recent Progress Incorporating Social Influence and Nonlinear Utility Functions

Explaining and predicting the choices of consumers is perhaps the most enduring researchnproblem faced by the behavioural sciences. Of the range of methods used to gather information about consumers preferences, choice based approaches are common place in bothnacademia and industry. For the most part, consumers preferences from this type of datanare typically evaluated assuming preferences for the attributes of alternatives in choice arenindependent as a necessity due to the limitations of the statistical model. Recent advancesnin choice modelling however present an opportunity to consider that there may be a latentnstructure to decision makers’ taste sensitivities (preference parameters) and further, behavioural decision theory may be useful in helping the researcher specify that structure. Thisnthesis documents three applications of structural choice modelling that both test behaviouralndecision theory as well as contribute new interpretations about how behavioural decision theory may manifest as structures within the latent dimensions of consumers utility function.nEach application considers the structure of the taste variation (preference heterogeneity)nin decision makers’ taste sensitivities towards the attributes of transport services. In eachncase, behavioural decision theory is drawn upon to first predict what structures might benexpected, and subsequently structural choice models are specified to represent theory. Thisnunique modelling approach allows the following types of research questions to be addressed:• How does the nature of the choice environment affect the taste sensitivities in decisionnmakers’ preferences for particular product/service attributes?• Are latent sources of variation in consumers taste sensitivities stable or dynamic?n• What are the drivers of commonalities between the decision makers’ taste sensitivitiesnfor the attributes of alternatives in choice?• Is there a relationship between the way consumers think about their priorities and thenway consumers make choices?These questions are explored using so-called structural choice models. Structural choicenmodels are type of factor-analyticn choice model (a very general form of mixed logit). Thenmodel allows for a parsimonious representation of the ways in which consumers vary in theirntastes and preferences. Representing the unobserved sources of taste variation as latent variables and specifying a structure to the latent variables offer great flexibility, including datanfrom the same consumers from different choice tasks. This allows the researcher, for example,nto specify latent variables general to several data generation processes and others unique tonone particular process. Thus, the extent to which attributes under different scenarios arenindeed treated by decision makers’ as the same can be assessed. From a policy perspective, the design of more nuanced policy responses may be possible given the insight into thenstructure of the heterogeneity in decision makers’ preferences. The thesis is presented in thenfollowing way. Firstly, the introduction provides an overview of behavioural decision theory,nchoice based methods, structural choice modelling and its general econometric specifications.nNext, the empirical results of three separate but related studies are presented. The studiesndocument applications of the structural choice model to representing testing latent variablenrepresentations of taste variation. Finally, the conclusion provides a summary of all thenresults as well as discusses the future of research in this area.n