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A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

1
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
2
Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Eng., K.N. Toosi University of Technology, Tehran 19697, Iran
3
Department of Computer Science and Engineering, Sejong University, Seoul 143-747, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(7), 808; https://doi.org/10.3390/electronics8070808
Received: 9 May 2019 / Revised: 17 June 2019 / Accepted: 28 June 2019 / Published: 19 July 2019
(This article belongs to the Section Computer Science & Engineering)
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Abstract

This study proposed a context-aware ontology-based route finding algorithm for self-driving tourists. In this algorithm, two ontologies—namely drivers’ experiences and required tourist services—were used according to tourist requirements. Trips were classified into business and touristic. The algorithm was then compared with Google Maps in terms of travel time and travel length for evaluation. The results showed that the proposed algorithm performed similarly to Google Maps in some cases of business trips and better in other cases, with a maximum 10-min travel time difference. In touristic trips, the capabilities of the proposed algorithm were far better than those of Google Maps. View Full-Text
Keywords: touristic trip; business trip; ontology; experience; route finding touristic trip; business trip; ontology; experience; route finding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Barzegar, M.; Sadeghi-Niaraki, A.; Shakeri, M.; Choi, S.-M. A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology. Electronics 2019, 8, 808.

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