scholarly journals Analysis of the Temporal and Geographical Transferability of Disaggregate Hurricane Evacuation Demand Models of Different Model Specification

2016 ◽  
Vol 4 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Ravindra Gudishala ◽  
Chester Wilmot
Keyword(s):  
Model Specification ◽  
Hurricane Evacuation ◽  
Demand Models

scholarly journals Hurricane evacuation demand models with a focus on use for prediction in future events

2016 ◽  
Vol 87 ◽  
pp. 90-101 ◽  
Author(s):  
Kecheng Xu ◽  
Rachel A. Davidson ◽  
Linda K. Nozick ◽  
Tricia Wachtendorf ◽  
Sarah E. DeYoung
Keyword(s):  
Hurricane Evacuation ◽  
Demand Models ◽  
Future Events

Choice Model Specification in Market-Based Engineering Design

2008 ◽  
Author(s):  
Joseph A. Donndelinger ◽  
Jeffrey A. Robinson ◽  
Luke A. Wissmann
Keyword(s):  
Engineering Design ◽  
Choice Model ◽  
Model Specification ◽  
Product Configuration ◽  
Random Utility ◽  
Market Demand ◽  
Prior Work ◽  
Demand Model ◽  
Engineering Design Problems ◽  
Demand Models

The application of market demand models in engineering design is now a well-established practice. One could consider the archetypical application to be a random utility model used in conjunction with a parametric design representation to optimize the design of a single product with respect to a risk-adjusted measure of profit. Much of the work in this area over the past decade has been focused on various extensions of this archetypical framework, such as problem decomposition and product family design. A wide variety of market demand models have been applied, including models derived from classic economic methods and random utility models spanning from multinomial logit through generalized extreme value to mixed logit. While there has been some discussion of the properties of the various choices of market demand models used in prior work, the most recent work in this area suggests that the consequences of market demand model specification in engineering design problems are both more significant than once realized and not yet fully understood. In this paper, we explore the consequences of market demand model specification specifically in the context of engineering design through both a review of prior work and an illustrative example problem featuring a market demand model parameterized in terms of reservation price. These results demonstrate that choices in market demand model specification — especially those relating to representation of customer heterogeneity — can lead to substantially different conclusions in a discrete product configuration design problem.

Design Optimization of a Laptop Computer Using Aggregate and Mixed Logit Demand Models With Consumer Survey Data

2007 ◽  
Author(s):  
Ching-Shin Shiau ◽  
Ian H. Tseng ◽  
Andrew W. Heutchy ◽  
Jeremy Michalek
Keyword(s):  
Consumer Preferences ◽  
Mixed Logit ◽  
Profit Maximization ◽  
Model Specification ◽  
Computer Design ◽  
Demand Model ◽  
Engineering Model ◽  
Laptop Computer ◽  
Demand Models ◽  
Discrete Part

Laptop computers are designed in a variety of shapes and sizes in order to satisfy diverse consumer preferences. Each design is optimized to attract consumers with a particular set of preferences for design tradeoffs. Gaining a better understanding of these tradeoffs and preferences is beneficial to both laptop designers and to consumers. This paper introduces an engineering model for laptop computer design and a demand model derived from a main-effects choice-based conjoint survey. Several demand model specifications are compared, including linear-in-parameters and discrete part-worth specifications for aggregate multinomial logit and mixed logit models. An integrated optimization scheme combines the engineering model with each demand model form for profit maximization. The solutions of different optimal laptop designs and market share predictions resulting from the unique characteristics of each demand model specification are examined and compared.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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