2016
DOI: 10.14569/ijacsa.2016.071116
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Development of Dynamic Real-Time Navigation System

Abstract: Abstract-This study aimed to develop a system that considers dynamic real-time situations to provide effective support for tourist activities. The conclusions of this study are summarized in the following three points: (1) The system was developed by integrating Web-GIS, social media, recommendation systems and AR terminals (smart glasses) into a single system, and operated in the center part of Yokohama City in Kanagawa Prefecture, Japan. It enabled the accumulation, sharing and recommendation of information … Show more

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Cited by 18 publications
(16 citation statements)
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“…Okuzono et al [6] took into consideration the preferences of several people using photos, and proposed a system that recommends sightseeing spots. Fujita et al [7], Sonobe et al [8] and Sasaki et al [9] proposed tourism support systems using augmented reality (AR). Among these, Fujita et al [7] used Web-GIS and social media in addition to AR in order to support sightseeing activities during normal occasions and evacuation in case of a disaster.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Okuzono et al [6] took into consideration the preferences of several people using photos, and proposed a system that recommends sightseeing spots. Fujita et al [7], Sonobe et al [8] and Sasaki et al [9] proposed tourism support systems using augmented reality (AR). Among these, Fujita et al [7] used Web-GIS and social media in addition to AR in order to support sightseeing activities during normal occasions and evacuation in case of a disaster.…”
Section: Related Workmentioning
confidence: 99%
“…Fujita et al [7], Sonobe et al [8] and Sasaki et al [9] proposed tourism support systems using augmented reality (AR). Among these, Fujita et al [7] used Web-GIS and social media in addition to AR in order to support sightseeing activities during normal occasions and evacuation in case of a disaster. Sasaki et al [9] integrated location-based AR and object-recognition AR and used pictograms.…”
Section: Related Workmentioning
confidence: 99%
“…When each user registers at first, he/she is asked to rank the eight evaluation items (satisfaction level, access, non-crowdedness, accessibility for those with special needs, cost effectiveness, atmosphere, amenity and recommendation degree, which are the same evaluation items of the evaluation system mentioned in the previous section) on five levels, and the preference information is created. This is set as the user's feature vector, while the evaluation value (evaluation information) created by the evaluation system in the previous section is set as the feature vector of sightseeing spots, and the degree of similarity between these values is calculated using Equation (8).…”
Section: Recommendation Systemmentioning
confidence: 99%
“…The conceptual diagram concerning this process is shown in Figure 7. Regarding the evaluation information of sightseeing spots and user preferences (or group preferences) accumulated in the database, the degree of similarity will be calculated using Equation (8) shown in Section 3.4.3. Furthermore, the sightseeing spot information and evaluation values of the top 20 in similarity will be selected, sorted (filtered) according to the value of priority evaluation items, and the top 10 sightseeing spots will be displayed as recommendation results.…”
Section: The Backend Of the Systemmentioning
confidence: 99%
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