MONTUR project: Dataset for understanding and forecasting tourist flows

Marco Alderighi, Tiziana Ciano, Massimiliano Ferrara and Domenico Santoro

PLOS ONE, 2025, vol. 20, issue 10, 1-12

Abstract: This study presents an advanced system for monitoring and forecasting tourist flows in the Aosta Valley using distributed sensor technologies, cameras, and machine learning algorithms. This innovative system is designed to provide real-time data on arrivals and presences throughout the region, helping to manage traffic and tourism resources more effectively. The research analyzes data collected from portals equipped for traffic detection. Through a multi-phase approach, the project integrates and analyzes over 41 million vehicle passages to support informed decisions for regional economic and social policies. Furthermore, computational processes were conducted to optimize the analysis of the vehicle flow, reducing the dataset and focusing on checkpoints and vehicle categories. This type of time series revealed high stationarity, allowing the use of the eXtreme Gradient Boosting (XGBoost) algorithm for more accurate forecasts than Deep Learning models and other Machine Learning algorithms, such as those highlighted in terms of MAE and MSE. The results represent a significant step forward in managing tourist flows and improving the Aosta Valley’s operational efficiency and visitor experience.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335190

DOI: 10.1371/journal.pone.0335190

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