Using Stated Preference and Revealed Preference Data Fusion Modelling in Health Care

2008 ◽  
pp. 217-234 ◽  
Author(s):  
Tami Mark ◽  
Joffre Swait
Keyword(s):  
Health Care ◽  
Data Fusion ◽  
Stated Preference ◽  
Revealed Preference ◽  
Preference Data

Analysis of Travel Time Saving Benefit by Understanding Individual Needs and Value of Activity Time: Case Study of Tokyo and Jakarta

2003 ◽  
Vol 1854 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Irwan Prasetyo ◽  
Daisuke Fukuda ◽  
Hirosato Yoshino ◽  
Tetsuo Yai
Keyword(s):  
Travel Time ◽  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Psychological Needs ◽  
Activity Time ◽  
Time Saving ◽  
Allocation Model ◽  
Preference Data

Quantification of the value of time (VOT) is important for measurement of the benefit of transportation projects in terms of travel time savings. In Japan, VOT is considered higher on weekends than on weekdays because on the weekend people have limited time to allocate to discretionary activities that are not normally done on weekdays, such as family care-related activities. In Indonesia, a culturally diverse country, providers and users seem to have different perceptions of VOT. A method of analyzing the value of activity time is presented. It argues that the benefit of travel time saving should be evaluated in more detail on weekends by considering the value of discretionary activities to explain these phenomena theoretically. Activity diary surveys were conducted in Tokyo, Japan, and Jakarta, Indonesia, to verify the influence of psychological needs on people's holiday activities. Finally, a time allocation model that uses the revealed preference data and a marginal activity choice model that uses stated preference data are proposed to calculate the value of activity time. The theories underpinning these models are Maslow's psychological needs, consumer theory in economics, and a discrete choice model. The empirical results show that an individual's priority of needs influences time allocation. In particular, the results show that in Tokyo, spending time with family on weekends is more valuable than other types of activities, while in Indonesia the value of spending time with family exceeds that of work time even on weekdays.

scholarly journals Econometrics of Stated Preferences

2019 ◽  
Author(s):  
Denzil G. Fiebig ◽  
Hong Il Yoo
Keyword(s):  
Stated Preference ◽  
Research Question ◽  
Effective Means ◽  
Revealed Preference ◽  
Cost Effective ◽  
Discrete Choice Experiments ◽  
Alternative Source ◽  
Preference Data ◽  
Stated Preference Methods ◽  
Realistic Situation

Stated preference methods are used to collect individual-level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article is data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.

scholarly journals Joint Estimation of Revealed and Stated Preference Data: An Application to Recreational Red Snapper Valuation

2003 ◽  
Vol 32 (2) ◽  
pp. 209-221 ◽  
Author(s):  
Dhazn Gillig ◽  
Richard Woodward ◽  
Teofilo Ozuna ◽  
Wade L. Griffin
Keyword(s):  
Stated Preference ◽  
Contingent Valuation Method ◽  
Revealed Preference ◽  
Estimation Procedure ◽  
Joint Estimation ◽  
Red Snapper ◽  
Preference Data ◽  
Valuation Method ◽  
Upper Limit ◽  
Stated Preference Data

This study extends the joint estimation of revealed and stated preference data literature by accounting for truncation in the revealed preference data. The analytical model and estimation procedure are used to estimate the value of recreational red snapper fishing in the Gulf of Mexico. This recreational red snapper valuation is decomposed into its direct and indirect components. As expected, the value of recreational red snapper fishing using the joint revealed-stated preference model proposed in this analysis is bracketed on the upper limit by the value obtained using the contingent valuation method and on the lower limit by the travel cost method. The results also indicate that the joint model improves the precision of estimated recreational red snapper valuation.

scholarly journals Choice Modeling for Usage Context-Based Design

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Christopher Hoyle ◽  
Bernard Yannou
Keyword(s):  
Hybrid Electric Vehicle ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Modeling ◽  
Modeling Framework ◽  
Preference Data ◽  
Design Paradigm ◽  
Usage Context ◽  
The Impact

Usage context-based design (UCBD) is an emerging design paradigm where usage context is considered as a critical part of driving factors behind customers’ choices. Here, usage context is defined as all aspects describing the context of product use that vary under different use conditions and affect product performance and/or consumer preferences for the product attributes. In this paper, we propose a choice modeling framework for UCBD to quantify the impact of usage context on customer choices. We start with defining a taxonomy for UCBD. By explicitly modeling usage context’s influence on both product performances and customer preferences, a step-by-step choice modeling procedure is proposed to support UCBD. Two case studies, a jigsaw example with stated preference data and a hybrid electric vehicle example with revealed preference data, demonstrate the needs and benefits of incorporating usage context in choice modeling.

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

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