Dave, K, Toner, JP and Chen, H (2018) The effect of attribute representation methods on noise valuation: A choice experiment study. Transportation Research Part D: Transport and Environment, 62. pp. 80-89. ISSN 1361-9209
Abstract
Traffic noise has been known to severely affect human population. The valuation of traffic noise pose a significant challenge in choice experiments as respondents have little understanding of the physical measure of noise and its associated perception. As a result, several techniques have been developed that represent noise using different methods, either based on the level of noise exposure or the respondent’s level of noise annoyance. This study examines the effect of different methods of attribute representation on respondents’ attribute understanding and valuation. The study is focussed on residential choice and residential view and sunlight are important attributes that are examined along with traffic noise. The study demonstrates that the methods of attribute representation have an important effect on respondents’ understanding of the attributes as well as in the subsequent valuation. It was found that attribute such as view is better represented using the location representation while noise is better represented using the linguistic method. Moreover, the method of attribute modelling also plays a significant role in the analysis as certain data input techniques are more suitable for some representation methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Ltd. This is an author produced version of a paper published in Transportation Research Part D: Transport and Environment. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Noise valuation, Choice experiments, Logit model, Attribute representation, View, Sunlight |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Apr 2018 09:33 |
Last Modified: | 16 Feb 2019 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trd.2018.01.011 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129441 |