Abstract

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO<inf>2</inf> emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. © 2015.


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https://api.elsevier.com/content/article/PII:S0968090X15001680?httpAccept=text/plain,
http://dx.doi.org/10.1016/j.trc.2015.04.027 under the license https://www.elsevier.com/tdm/userlicense/1.0/
https://www.rug.nl/research/portal/en/publications/personalised-feedback-and-ecodriving(179249b7-8a2a-42e4-b534-05531572e042).html,
https://repository.tudelft.nl/view/tno/uuid:22f6c389-cff8-4c9a-96af-4ebf45265979,
https://www.narcis.nl/publication/RecordID/oai%3Atudelft.nl%3Auuid%3A22f6c389-cff8-4c9a-96af-4ebf45265979,
https://trid.trb.org/view.aspx?id=1375416,
https://core.ac.uk/display/85545963,
https://academic.microsoft.com/#/detail/857163924
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Published on 01/01/2015

Volume 2015, 2015
DOI: 10.1016/j.trc.2015.04.027
Licence: Other

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