Are Generalized Extreme Value Class Discrete Choice Demand Models Rationalizable as a Representative Consumer's Behavior?

2017 ◽  
Author(s):  
Joonhwi Joo ◽  
Kyeongbae Kim
Keyword(s):  
Discrete Choice ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Demand Models ◽  
Discrete Choice Demand

Enhancing Discrete Choice Demand Modeling for Decision-Based Design

2003 ◽  
Author(s):  
Henk Jan Wassenaar ◽  
Wei Chen ◽  
Jie Cheng ◽  
Agus Sudjianto
Keyword(s):  
Discrete Choice ◽  
Market Research ◽  
Ford Motor Company ◽  
Demand Model ◽  
Demand Modeling ◽  
Product Demand ◽  
Demand Models ◽  
Discrete Choice Demand ◽  
Decision Based Design ◽  
Key Customer

Our research is motivated by the need for developing a rigorous Decision-Based Design framework and the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring. Even though demand modeling techniques exist in market research, little work exists on product demand modeling that addresses the specific needs of engineering design in particular that facilitates engineering decision-making. Building upon our earlier work on using the discrete choice analysis approach to demand modeling, in this work, we provide detailed guidelines for implementing the discrete choice demand modeling approach in product design. The modeling of a hierarchy of product attributes is introduced to cascade customer desires to specific key customer attributes that can be represented using engineering language. To improve the predictive capability of demand models, we propose to use the Kano method for providing the econometric justification when selecting the shape of the customer utility function. A real (passenger) vehicle engine case study, developed in collaboration with the market research firm J.D. Power & Associates and Ford Motor Company, demonstrates the proposed approaches. The example focuses on demand analysis and does not reach beyond the key customer attribute level. The obtained demand model is shown to be satisfactory through cross validation.

scholarly journals Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes

2021 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Xingchen Yan ◽  
Xiaofei Ye ◽  
Jun Chen ◽  
Tao Wang ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Information Criterion ◽  
Lateral Position ◽  
Extreme Value ◽  
Optimal Number ◽  
Generalized Extreme Value ◽  
Vehicle Type ◽  
Lane Width ◽  
Bike Sharing ◽  
Mode Of Transport ◽  
Best Fit

Cycling is an increasingly popular mode of transport as part of the response to air pollution, urban congestion, and public health issues. The emergence of bike sharing programs and electric bicycles have also brought about notable changes in cycling characteristics, especially cycling speed. In order to provide a better basis for bicycle-related traffic simulations and theoretical derivations, the study aimed to seek the best distribution for bicycle riding speed considering cyclist characteristics, vehicle type, and track attributes. K-means clustering was performed on speed subcategories while selecting the optimal number of clustering using L method. Then, 15 common models were fitted to the grouped speed data and Kolmogorov–Smirnov test, Akaike information criterion, and Bayesian information criterion were applied to determine the best-fit distribution. The following results were acquired: (1) bicycle speed sub-clusters generated by the combinations of bicycle type, bicycle lateral position, gender, age, and lane width were grouped into three clusters; (2) Among the common distribution, generalized extreme value, gamma and lognormal were the top three models to fit the three clusters of speed dataset; and (3) integrating stability and overall performance, the generalized extreme value was the best-fit distribution of bicycle speed.

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