Basiri, A;
Amirian, P;
Winstanley, A;
Moore, T;
(2017)
Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data.
Journal of Ambient Intelligence and Humanized Computing
10.1007/s12652-017-0550-0.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s12652-017-0550-0 |
| Publisher version: | https://doi.org/10.1007/s12652-017-0550-0 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2017. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Ambient services, Tourist guidance, Trajectory data mining, Touristic point of interest (PoI), Spatio-temporal data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10039910 |
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