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.

A Framework for Choice Modeling in Usage Context-Based Design

2010 ◽  
Author(s):  
Lin He ◽  
Christopher Hoyle ◽  
Wei Chen ◽  
Jiliang Wang ◽  
Bernard Yannou
Keyword(s):  
Data Collection ◽  
Product Design ◽  
Consumer Choice ◽  
Choice Model ◽  
Consumer Preferences ◽  
Choice Modeling ◽  
Product Performance ◽  
Choice Data ◽  
Modeling Framework ◽  
Usage Context

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. A framework and a step-by-step procedure for implementing consumer choice modeling in UCBD are presented in this work. To implement the proposed approach, methods for common usage identification, data collection, linking performance with usage context, and choice model estimation are developed. For data collection, a method of try-it-out choice experiments is presented. This method is necessary to account for the different choices respondents make conditional on the given usage context, which allows us to examine the influence of product design, customer profile, usage context attributes, and their interactions, on the choice process. Methods of data analysis are used to understand the collected choice data, as well as to understand clusters of similar customers and similar usage contexts. The choice modeling framework, which considers the influence of usage context on both the product performance, choice set and the consumer preferences, is presented as the key element of a quantitative usage context-based design process. In this framework, product performance is modeled as a function of both the product design and the usage context. Additionally, usage context enters into an individual customer’s utility function directly to capture its influence on product preferences. The entire process is illustrated with a case study of the design of a jigsaw.

Quantifying the Impact of the COVID-19 Pandemic on Passenger Vehicle Drivers’ Willingness to Pay for Travel Time Savings and Reliability

2021 ◽  
pp. 036119812110388
Author(s):  
Tristan Cherry ◽  
Mark Fowler ◽  
Claire Goldhammer ◽  
Jeong Yun Kweun ◽  
Thomas Sherman ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Travel Time ◽  
Travel Behavior ◽  
Local Governments ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Travel Time Reliability ◽  
Travel Time Savings ◽  
Time Savings ◽  
The Impact

The COVID-19 pandemic has fundamentally disrupted travel behavior and consumer preferences. To slow the spread of the virus, public health officials and state and local governments issued stay-at-home orders and, among other actions, closed nonessential businesses and educational facilities. The resulting recessionary effects have been particularly acute for U.S. toll roads, with an observed year-over-year decline in traffic and revenue of 50% to 90% in April and May 2020. These disruptions have also led to changes in the types of trip that travelers make and their frequency, their choice of travel mode, and their willingness to pay tolls for travel time savings and travel time reliability. This paper describes the results of travel behavior research conducted on behalf of the Virginia Department of Transportation before and during the COVID-19 pandemic in the National Capital Region of Washington, D.C., Maryland, and Northern Virginia. The research included a stated preference survey to estimate travelers’ willingness to pay for travel time savings and travel time reliability, to support forecasts of traffic and revenue for existing and proposed toll corridors. The survey collected data between December 2019 and June 2020. A comparison of the data collected before and during the pandemic shows widespread changes in travel behavior and a reduction in willingness to pay for travel time savings and travel time reliability across all traveler types, particularly for drivers making trips to or from work. These findings have significant implications for the return of travelers to toll corridors in the region and future forecasts of traffic and revenue.

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.

On Usage Context of Hybrid Electric Vehicles in Choice Studies

2011 ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Guenter Conzelmann
Keyword(s):  
Hybrid Electric Vehicle ◽  
Choice Model ◽  
Principal Component ◽  
Choice Modeling ◽  
Multiple Sources ◽  
Hybrid Electric ◽  
Using Data ◽  
Household Travel ◽  
Usage Context ◽  
The Relationship

Considering usage context attributes in choice modeling has been shown to be important when product performance highly depends on the usage context. To build a reliable choice model, it is critical to first understand the relationship between usage context attributes and customer profile attributes, then to identify the market segmentation characterized by both sets of attributes, and finally to construct a choice model by integrating data from multiple sources. This is a complex procedure especially when a large number of customer attributes are potentially influential to the product choice. Using the hybrid electric vehicle (HEV) as an example, this paper presents a systematic procedure and the associated data analysis techniques for implementing each of the above steps. Usage context and customer profile attributes extracted from both National Household Travel Survey (NHTS) and Vehicle Quality Survey (VQS) data are first analyzed to understand the relationship between usage context attributes and customer profile attributes. Next the principal component analysis is utilized to identify the key characteristics of hybrid vehicle drivers, and to determine the market segmentations of HEV and the critical attributes to include in choice models. Before the two sets of data are combined for choice modeling, statistical analysis is used to test the compatibility of the two datasets. A pooled choice model created by incorporating usage context attributes illustrates the benefits of context-based choice modeling using data from multiple sources. Even though NHTS and VQS have been used in the literature to study transportation patterns and vehicle quality ratings, respectively, this work is the first to explore how they may be used together to benefit the study of customer preference for HEVs.

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.

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