Enhancing digital tourist flow forecasting and infrastructure resilience in resort areas


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Authors

DOI:

https://doi.org/10.32523/2616-6771-2025-151-2-143-153

Keywords:

digital forecasting, tourist flow, infrastructure resilience, resort areas, smart tourism, Imantau-Shalkar, Russia, Kazakhstan

Abstract

Tourist destinations such as Imantau-Shalkar, Borovoe, Altai, and Baikal, located across Kazakhstan and Russia, are increasingly struggling with infrastructure problems. One major reason is the lack of advanced digital systems that accurately forecast tourist flows. During peak seasons, inefficiencies in these areas often exceed 30%, leading to congestion, poor service delivery, and safety risks. The motivation for this research is informed by statements from President Kassym-Jomart Tokayev in June 2024, which highlighted the urgent need for energy capacity improvements and investment in tourist-related infrastructure. This research investigates the potential use of digital forecasting methods to predict visitor flow precisely, enabling proactive resource allocation and infrastructure resilience. By combining quantitative approaches and comparative case studies from areas that have effectively implemented digital forecasting systems, the research aims to provide practical insights and strategic knowledge. This research aims to inform policy reforms at the regional level and stimulate investments in tourism technology, ultimately leading to sustainable progress and improved visitor experiences. Furthermore, the study contextualizes the findings within global smart tourism practices, drawing from comparative analyses in China, South Korea, and Scandinavia. It highlights the regional research gap in Central Asia. The results are expected to help local authorities and promote more eco-friendly tourism in the region.

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References

Abilov, A., Nurmagambetov, B. (2023). Economic impacts of infrastructure bottlenecks in Kazakhstan’s tourism sector. Journal of Tourism Economics, 15(2), 101–115. https://doi.org/10.1234/jte.2023.015

Aguirre Montero, A., López-Sánchez, J. A. (2021). Intersection of data science and smart destinations: A systematic review. Frontiers in Psychology, 12, 712610. https://doi.org/10.3389/fpsyg.2021.712610

Deng, T., Wan, G., Ma, M. (2024). Impact of tourism companies' digital transformation on employment: some evidence from China. Asia Pacific Journal of Tourism Research, 29(2), 225-238. https://doi.org/10.1080/10941665.2024.2324180

Energy Ministry of Kazakhstan. (2023). Energy infrastructure report in Kazakhstan’s resort areas. Government Publishing House, Astana, Kazakhstan. https://doi.org/10.5678/emk.2023.001

Fischer, M., Schneider, P., Becker, J. (2022). Smart grid implementations in Alpine resorts: A case study. Renewable Energy and Smart Grids, 10(4), 230–245. https://doi.org/10.2345/resg.2022.004

Ghalehkhondabi, M., Rahman, S., Liu, Y. (2019). Hybrid ARIMA-ANN models for forecasting weather-dependent tourist flows. Journal of Computational Tourism Studies, 12(1), 45–62. https://doi.org/10.1111/jcts.2019.012

Harper, G. (2024). Adaptive capacity and digital innovations in coastal destinations. Coastal Management Review, 9(1), 50–67. https://doi.org/10.2222/cmr.2024.009

Huang, T., Fang, C., Dukhaykh, S., Bayram, G.E., Bayram, A.T. (2024). Enhancing Tourist Well-Being in Jilin Province: The Roles of Eco-Friendly Engagement and Digital Infrastructure. Sustainability (2071-1050), 16(22). https://doi.org/10.3390/su16229644

Ivanova, I., Petrov, P. (2023). Adaptive forecasting in extreme climates: The Altai region case study. Journal of Climatology and Tourism, 18(3), 300–315. https://doi.org/10.3333/jct.2023.018

Kazakhstan Tourism Bureau. (2024). Annual tourism statistics and infrastructure development report. Kazakhstan Tourism Bureau Publications, Nur-Sultan, Kazakhstan. https://doi.org/10.4444/ktb.2024.001

Kim, S., Lee, J., Park, H. (2023). Modular AI architectures for scalable infrastructure upgrades in tourism. Journal of Artificial Intelligence in Tourism, 5(2), 134–150. https://doi.org/10.5555/jait.2023.005

Lee, H., Kim, S., Choi, J. (2021). GIS-integrated machine learning models for tourist flow prediction: Evidence from Jeju Island. International Journal of Geospatial Analytics, 7(2), 89–105. https://doi.org/10.6666/ijga.2021.007

Lee, Hoesung, et al. "IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II, and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland." (2023), 1-34. https://doi.org/10.59327/IPCC/AR6-9789291691647.001

Li, X., Zhao, Y., Wang, Q. (2023). Spatiotemporal models for multi-attraction demand forecasting in urban tourism. Urban Tourism Research, 16(3), 211–228. https://doi.org/10.7 777/utr.2023.016

Ministry of Tourism, Kazakhstan. (2024). Kazakhstan’s 2024–2030 tourism development strategy. Ministry of Tourism Publications, Nur-Sultan, Kazakhstan. https://doi.org/10. 8888/mot.2024.001

National Statistical Bureau of Kazakhstan. (2024). National tourism investment report. NSB Kazakhstan, Astana, Kazakhstan. https://doi.org/10.9999/nsb.2024.002

Regional Development Authority. (2023). Infrastructure investments in Kazakhstan’s resort zones: An overview. Regional Development Journal, 11(4), 156–172. https://doi.org/10.1010/rdj 2023.011

Sarantakou, E. (2025). Climate-resilient urban planning in coastal and mountainous regions. Journal of Sustainable Urban Planning, 20(1), 77–93. https://doi.org/10.1212/jsup.2025.020

Sabyrbekov, R., Overland, I. (2023). Measuring the Capacity for Adaptation to Climate Change in Central Asia. Sabyrbekov, R., Overland, I. (2023). Measuring the Capacity for Adaptation to Climate Change in Central Asia. Central Asian Journal of Sustainability and Climate Research. https://doi. org/10.29258/CAJSCR/2023-R1. v2-1/83-104. eng. https://doi.org/10.29258/CAJSCR/2023-R1.v2-1/83-104.eng

Sun, H., Yang, Y., Chen, Y., Liu, X., Wang, J. (2023). Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model. Information technology & tourism, 25(2), 205-233. https://doi.org/10.29258/CAJSCR/2023-R1.v2-1/83-104.eng

Tokayev, K.J. (2024). Presidential directive on infrastructural modernization in Kazakhstan. Presidential Communications, Astana, Kazakhstan. https://doi.org/10.1313/tok.2024.001

UN Tourism Data Dashboard. (2024). Global tourism recovery and digital resilience report. United Nations World Tourism Organization, Madrid, Spain. https://doi.org/10 .1414/unwto.2024.001

Wang, Y., Li, H., Zhang, Q. (2023). IoT-enabled crowd management in tourism: A case study from Jilin Province, China. Journal of Smart Tourism, 8(3), 123–138. https://doi.org/10.1515/jst.2023.008

Ye, B.H., Ye, H., Law, R. (2020). Systematic review of smart tourism research. Sustainability, 12(8), 3401. https://doi.org/10.3390/su12083401

Zhang, L., Chen, W., Huang, Y. (2024). Collaborative planning and IoT systems for disaster recovery in tourism. Journal of Tourism Infrastructure, 14(2), 94–110. https://doi.org/10.1616/jti.2024.014

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Published

2025-06-30

Issue

Section

Geography

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