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Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Basiri, Anahid; Amirian, Pouria; Winstanley, Adam; Moore, Terry

Authors

Anahid Basiri

Pouria Amirian

Adam Winstanley

Terry Moore



Abstract

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data.

Citation

Basiri, A., Amirian, P., Winstanley, A., & Moore, T. (in press). Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-017-0550-0

Journal Article Type Article
Acceptance Date Jul 11, 2017
Online Publication Date Sep 1, 2017
Deposit Date Sep 8, 2017
Publicly Available Date Mar 28, 2024
Journal Journal of Ambient Intelligence and Humanized Computing
Print ISSN 1868-5137
Electronic ISSN 1868-5145
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s12652-017-0550-0
Keywords Ambient services, Tourist guidance, Trajectory data mining, Touristic point of interest, Spatio-temporal data
Public URL https://nottingham-repository.worktribe.com/output/879782
Publisher URL https://link.springer.com/article/10.1007/s12652-017-0550-0

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