Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data
Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data
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.
Ambient services, Spatio-temporal data, Tourist guidance, Touristic point of interest (PoI), Trajectory data mining
413-427
Basiri, Anahid
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Amirian, Pouria
2a34b85e-30e8-4ced-8aa2-83588a3b6277
Winstanley, Adam
2cee1600-8497-4eb6-9516-219989474b16
Moore, Terry
382aba39-d3c1-4b25-9522-c17363d23fd1
1 April 2018
Basiri, Anahid
3f1ff8a7-1db0-4ac4-aaea-7c564d5ed857
Amirian, Pouria
2a34b85e-30e8-4ced-8aa2-83588a3b6277
Winstanley, Adam
2cee1600-8497-4eb6-9516-219989474b16
Moore, Terry
382aba39-d3c1-4b25-9522-c17363d23fd1
Basiri, Anahid, Amirian, Pouria, Winstanley, Adam and Moore, Terry
(2018)
Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data.
Journal of Ambient Intelligence and Humanized Computing, 9 (2), .
(doi:10.1007/s12652-017-0550-0).
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.
Text
s12652-017-0550-0
- Version of Record
More information
Accepted/In Press date: 11 July 2017
e-pub ahead of print date: 1 September 2017
Published date: 1 April 2018
Keywords:
Ambient services, Spatio-temporal data, Tourist guidance, Touristic point of interest (PoI), Trajectory data mining
Identifiers
Local EPrints ID: 419628
URI: http://eprints.soton.ac.uk/id/eprint/419628
ISSN: 1868-5137
PURE UUID: e78c3fa2-5947-4724-897b-63557bdba171
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Date deposited: 17 Apr 2018 16:30
Last modified: 17 Mar 2024 12:03
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Contributors
Author:
Anahid Basiri
Author:
Pouria Amirian
Author:
Adam Winstanley
Author:
Terry Moore
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