An AI-Based Platform Architecture with Situational Awareness for Travel Plans
- Authors
- Jung, Tae Moon; Joe, Inwhee
- Issue Date
- Jan-2023
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Congestion; Mobile travel platforms; Ontology; Personalized customization services; Public data analysis; Situational awareness
- Citation
- Lecture Notes in Networks and Systems, v.596 LNNS, pp.376 - 384
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Networks and Systems
- Volume
- 596 LNNS
- Start Page
- 376
- End Page
- 384
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182532
- DOI
- 10.1007/978-3-031-21435-6_32
- ISSN
- 2367-3370
- Abstract
- Demand for non-face-to-face services is increasing in all industries due to the impact of the global pandemic caused by COVID-19 over the past three years. This phenomenon also occurs in the travel industry without exception. Travel consumption patterns are changing in the form of FIT (Foreign Independent Tour) rather than package tours provided by existing travel agencies. Now, we are entering the era of travel tech using smartphones. To this end, technological efforts are being made to simultaneously address the two characteristics of individual free travel and non-face-to-face situations. In this paper, we propose an AI-based platform architecture with situation awareness for travel plans. It provides a personalized service using the ontology-based mobile service so that tourists can make travel plans according to their own circumstances, after considering the situation of individual tourists through sensor collection data while analyzing the congestion situation of tourist destinations through public data. The mobile platform consists of a total of four layers (Infrastructure, Cross-Cutting Infrastructure, Domain, Application), and has been implemented in an environment of Android 8.0 or higher and iOS 11.0 or higher through the AWS website.
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