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 A Joint Estimation Method to Combine Dichotomous Choice CVM Models with Count Data TCM Models Corrected for Truncation and Endogenous Stratification

2008 ◽  
Vol 40 (2) ◽  
pp. 681-695 ◽  
Author(s):  
Juan Marcos González ◽  
John B. Loomis ◽  
Armando González-Cabán
Keyword(s):  
Count Data ◽  
Stated Preference ◽  
Estimation Method ◽  
Joint Estimation ◽  
Site Quality ◽  
Count Data Models ◽  
Preference Data ◽  
Estimation Efficiency ◽  
Stated Preference Data ◽  
Endogenous Stratification

We update the joint estimation of revealed and stated preference data of previously published research to allow for joint estimation of the Travel Cost Method (TCM) portion using count data models. The TCM estimation also corrects for truncation and endogenous stratification as well as overdispersion. The joint estimation allows for testing consistency of behavior between revealed and stated preference data rather than imposing it. We find little gain in estimation efficiency, but our joint estimation might make a significant improvement in estimation efficiency when the contingent valuation scenarios involve major changes in site quality not reflected in the TCM data.

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

2020 ◽  
Vol 2674 (7) ◽  
pp. 494-503 ◽  
Author(s):  
Daniel J. Reck ◽  
Kay W. Axhausen
Keyword(s):  
Public Transportation ◽  
Modular Design ◽  
Stated Preference ◽  
Revealed Preference ◽  
Central Question ◽  
Preference Data ◽  
Stated Preference Data ◽  
Shared Mobility ◽  
Bike Sharing ◽  
Season Tickets

Mobility as a service (MaaS) seeks to integrate emerging shared mobility modes with existing public transportation (PT). Decisive to its uptake will be attractive subscription plans that cater for heterogeneous mobility needs. Research on willingness to pay for such plans has commenced, yet remains divided on a central question: how much to include of which mode, and how? Complementing previous research building on stated preference data, in this study revealed preference data is used to analyze the viability of different subscription plan components (PT, car-sharing, bike-sharing, taxi), modes of inclusion (budgets in minutes and season tickets) and subscription cycles (weekly, monthly). PT season tickets are found to be viable for 83% of all respondents. Interestingly, the viability of minute budgets of car- and bike-sharing depends on subscription cycle length. Using a monthly subscription cycle, car-/bike-sharing appears viable to include in a bundle for 35%/31% of all respondents, respectively. Using a weekly subscription cycle, these figures drop to 1.4%/0.4%, respectively, as weekly variation in demand is much higher than monthly variation. In contrast to many current MaaS pilots, taxi use remains too infrequent to include as recurring credit in MaaS plans. Rather, pay-as-you-go is the economically more sensible option for consumers. This research therefore challenges the idea of all-inclusive mobility flat rates and suggests a more modular design.

scholarly journals A Model of Car and P&BR Choice in Commuting Trips Combining the Experiment Day Data with the Stated Preference Data

1999 ◽  
Vol 16 ◽  
pp. 955-961
Author(s):  
Hongzhi Guan ◽  
Kazuo Nishii ◽  
Atsushi Tanaka ◽  
Takeshi Morikawa
Keyword(s):  
Stated Preference ◽  
Preference Data ◽  
Stated Preference Data
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