Manuscript Number : SHISRRJ258315

A Predictive Framework for Post-Pandemic Tourism Recovery : Integrating Machine Learning and Visitor Behavior Analytics

Authors(3) :-Ifeoluwa Oreofe Adekuajo, Bisayo Oluwatosin Otokiti, Friday Okpeke

The COVID-19 pandemic caused unprecedented disruptions to global tourism, leading to significant economic losses and long-term behavioral shifts in travel preferences. As destinations navigate the path to recovery, there is a critical need for data-driven strategies that can anticipate tourism trends and inform policy decisions. This study proposes a predictive framework for post-pandemic tourism recovery by integrating machine learning (ML) techniques with visitor behavior analytics. The framework harnesses real-time travel data, online reviews, and booking behavior from diverse platforms to build a robust, adaptive model capable of forecasting recovery trajectories across various regions and tourism sectors. We developed a supervised learning model incorporating time series analysis and natural language processing (NLP) to analyze both structured (e.g., bookings, cancellations, visitor flows) and unstructured (e.g., sentiment in online reviews) data. Key features such as traveler demographics, seasonality, mobility trends, health policy changes, and public sentiment were used to train and validate the model. The framework demonstrated high predictive accuracy in estimating short- and medium-term tourism demand, identifying emerging traveler preferences, and flagging regions requiring targeted intervention. This research contributes to national tourism resilience by providing a scalable tool for policymakers and tourism boards to optimize resource allocation, safety protocols, and marketing strategies in real-time. By identifying behavioral signals and demand shifts early, destinations can adapt swiftly, enhance visitor experiences, and maintain competitive advantage in a volatile environment. Moreover, the framework enables scenario testing under different policy and health conditions, offering insights into the potential impact of travel restrictions, vaccination rates, or geopolitical events. The integration of machine learning and behavioral analytics equips stakeholders with a forward-looking approach to tourism recovery, grounded in empirical evidence and agile response capabilities. This work underscores the value of technological innovation in supporting sustainable economic recovery and highlights the author’s technical competence to contribute to national tourism policy, resilience planning, and strategic forecasting.

Authors and Affiliations

Ifeoluwa Oreofe Adekuajo
Ministry of Art Culture and Tourism, Ekiti, Nigeria
Bisayo Oluwatosin Otokiti
Regent College. London, UK
Friday Okpeke
Independent Researcher, UK

Tourism Recovery, Machine Learning, Visitor Behavior Analytics, Post-Pandemic Tourism, Real-Time Travel Data, Predictive Modeling, Natural Language Processing, Economic Resilience, Policy Optimization, Tourism Forecasting.

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Publication Details

Published in : Volume 8 | Issue 3 | May-June 2025
Date of Publication : 2025-05-20
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 24-66
Manuscript Number : SHISRRJ258315
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Ifeoluwa Oreofe Adekuajo, Bisayo Oluwatosin Otokiti, Friday Okpeke, "A Predictive Framework for Post-Pandemic Tourism Recovery : Integrating Machine Learning and Visitor Behavior Analytics", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 8, Issue 3, pp.24-66, May-June.2025
URL : https://shisrrj.com/SHISRRJ258315

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