Artificial intelligence as a tool for public management in tourism
EDN: LREUNA DOI: 10.22394/2071-2367-2022-17-5-172-182
Keywords:
innovation, neural network, machine learning, tourism, computer vision, big data analysisAbstract
Digital transformation of public and municipal management en- tails wide implementation of the tech- nologies related to artificial intelligence (AI), such as neural networks, machine learning algorithms and computer vision The purpose of the article is to review the results of foreign and domestic theoretical research, as well as practical developments in the field of AI, which can be used by public authorities in the regulation of such an economic industry as tourism. The re- search methodology is the analysis and identification of prognostic and classifica- tion models built with the help of these tools, which allow reaching a new level in decision making. As a result, a technologi- cal vector for the development of domestic tourism in the regions is presented. In par- ticular, the introduction of AI technologies will allow public authorities not only to measure the anthropogenic load, conduct environmental monitoring of recreational areas and model their sustainable develop- ment, but also improve the safety of tour- ists. It is important that neural networks are able to solve the problem of false reviews, which will positively affect the quality of information available to Internet users. Thus, AI is taking shape as a new techno- logical paradigm that underlies manage- ment processes in various fields, including tourism.
References
Хамидов Э. Х. Глубокое обучение: понятие и применение // Моло- дой ученый. – 2020. – № 37 (327). – С. 8-11. – URL:
https://moluch.ru/archive/327/73499/ (дата обращения: 17.05.2022).
Муминова С. Р., Феоктистова В. М., Вагина У. В. Инновации в ту- ризме на основе информационных технологий // Сервис в России и за рубежом. – 2018. – Т. 12 (1). – С. 6-15. DOI: 10.24411/1995-042X-2018-
Su, Xiaoyan. (2020) Simulation of economic development of tourism industry based on FPGA and machine learning. Microprocessors and Mi- crosystems. 10.1016/j.micpro.2020.103523.
Luo, Yuyan& He, Jinjie & Mou, Yu& Wang, Jun & Liu, Tao (2021) Explor- ing China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Management Perspec- tives. 10.1016/j.tmp.2020.100769.
Bi, Jian-Wu & Liu, Yang & Li, Hui (2020) Daily tourism volume forecast- ing for tourist attractions. Annals of Tourism Research. 10.1016/j.annals.2020.102923.
Альмухамедова О. А. Применение нейросетевых систем искус- ственного интеллекта в достижении устойчивого развития туризма // Сервис в России и за рубежом. – 2021. – Т. 15. – № 3. – С. 7-17. – DOI:
24412/1995-042X-2021-3-7-17.
Khorsand, Ramina & Rafiee, Majid & Kayvanfar, Vahid (2020) Insights into TripAdvisor's online reviews: The case of Tehran's hotels. Tourism Management Perspectives. 10.1016/j.tmp.2020.100673.
Budhi, Gregorius Satia & Chiong, Raymond& Wang, Zuli & Dhakal, Sandeep (2021) Using a hybrid content-based and behaviour-based featur- ing approach in a parallel environment to detect fake reviews. Electronic Commerce Research and Applications. 10.1016/j.elerap.2021.101048.
Wang, Ning & Yang, Jun & Kong, Xuefeng& Gao, Ying (2022) A fake re- view identification framework considering the suspicion degree of reviews with time burst characteristics. Expert Systems with Applications. 10.1016/j.eswa.2021.116207.
Gómez, Diego & Salvador, Pablo& Sanz, Julia& Casanova, José Luis (2021) A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods. Environmental Pollution. 10.1016/j.envpol.2021.117489.
Zhang, Kun & Lin, Zhibin & Zhang, Jinyi (2021) Tourist gaze through computer vision: Differences between Asian, North American, and European tourists. Annals of Tourism Research. 10.1016/j.annals.2020.103039.
Payntar, Nicole D. & Hsiao, Wei-Lin& Covey, R. Alan & Grauman, Kris- ten (2021) Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco, Peru. Tourism Management. 10.1016/j.tourman.2020.104165.
Zhang, Yuan & Yang, Haisheng& Wang, Guangming (2021) Monitoring and management of high-end tourism in protected areas based on 3D sensor image collection. Displays. 10.1016/j.displa.2021.102089.
Lin, Yigang (2020) Automatic recognition of image of abnormal situa- tion in scenic spots based on Internet of things. Image and Vision Computing. 10.1016/j.imavis.2020.103908.
Abkarian, Hoseb & Tahlyan, Divyakant& Mahmassani, Hani& Smilowitz, Karen (2022) Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS track- ing data. Tourism Management. 10.1016/j.tourman.2021.104421.
Barnes, Stuart J. & Kirshner, Samuel Nathan (2021) Understanding the impact of host facial characteristics on Airbnb pricing: Integrating facial im- age analytics into tourism research. Tourism Management. 10.1016/j.tourman.2020.104235.
Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay& Shankar, Ravi & Gupta, Shivam (2021) Examining the predictors of successful Airbnb book- ings with Hurdle models: Evidence from Europe, Australia, USA and Asia-
Pacific cities. Journal of Business Research. 10.1016/j.jbusres.2021.08.035.
Sánchez-Medina, Agustín J.& C-Sánchez, Eleazar.(2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management. 10.1016/j.ijhm.2020.102546.
Huang, Liyao & Zheng, Weimin (2021) Novel deep learning approach for forecasting daily hotel demand with agglomeration effect. International Journal of Hospitality Management. 10.1016/j.ijhm.2021.103038.
Al Shehhi, Mohammed& Karathanasopoulos, Andreas (2020) Forecast- ing hotel room prices in selected GCC cities using deep learning. Journal of Hospitality and Tourism Management. 10.1016/j.jhtm.2019.11.003.
Gaur, Loveleen & Afaq, Anam& Solanki, Arun & Singh, Gurmeet& Sharma, Shavneet & Jhanjhi, N.Z. & My, Hoang Thi & Le, Dac-Nhuong (2021) Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels. Computers & Electrical Engineering. 10.1016/j.compeleceng.2021.107374.
Zhang, Chenxi & Xu, Zeshui & Gou, Xunjie & Chen, Shuixia (2021) An online reviews-driven method for the prioritization of improvements in ho- tel services. Tourism Management. 10.1016/j.tourman.2021.104382.
Ivanko, Dmytro & Sørensen, Åse Lekang & Nord, Natasa (2020) Select- ing the model and influencing variables for DHW heat use prediction in ho- tels in Norway. Energy and Buildings. 10.1016/j.enbuild.2020.110441.
Gunter, Ulrich& Zekan, Bozana (2021) Forecasting air passenger numbers with a GVAR model. Annals of Tourism Research. 10.1016/j.annals.2021.103252.
Yuan, Yalong & Yang, Min& Feng, Tao & Rasouli, Soora& Li, Dawei & Ruan, Xinpei (2021) Heterogeneity in passenger satisfaction with air-rail in- tegration services: Results of a finite mixture partial least squares model. Transportation Research Part A: Policy and Practice. 10.1016/j.tra.2021.03.003.
Hamidov, E. H. (2020) Glubokoe obuchenie: ponyatie i primenenie [Deep learning: concept and application] // Molodoj uchenyj [Young scien- tist]. № 37 (327). P. 8-11. URL: https://moluch.ru/archive/327/73499/ (da- ta obrashcheniya: 17.05.2022). (In Russ.)
Muminova, S. R., Feoktistova, V. M., Vagina, U. V. (2018) Innovacii v tur- izme na osnove informacionnyh tekhnologij [Innovations in tourism based on information technologies] // Servis v Rossii i za rubezhom [Service in Russia and abroad]. Vol. 12 (1). P. 6-15. DOI: 10.24411/1995-042X-2018- 10101. (In Russ.)
Su, Xiaoyan. (2020) Simulation of economic development of tourism industry based on FPGA and machine learning. Microprocessors and Mi- crosystems. 10.1016/j.micpro.2020.103523.
Luo, Yuyan& He, Jinjie & Mou, Yu& Wang, Jun & Liu, Tao (2021) Explor- ing China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Management Perspec- tives. 10.1016/j.tmp.2020.100769.
Bi, Jian-Wu & Liu, Yang & Li, Hui (2020) Daily tourism volume forecast- ing for tourist attractions. Annals of Tourism Research. 10.1016/j.annals.2020.102923.
Al'muhamedova, O. A. (2021) Primenenie nejrosetevyh sistem is- kusstvennogo intellekta v dostizhenii ustojchivogo razvitiya turizma [Appli- cation of neural network systems of artificial intelligence in achieving sus- tainable tourism development] // Servis v Rossii i za rubezhom [Service in Russia and abroad]. Vol. 15. № 3. P. 7-17. DOI: 10.24412/1995-042X-2021- 3-7-17. (In Russ.)
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Muminova S. R.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
