Wireless Crowd Detection for Smart Overtourism Mitigation
Tomás Mestre Santos, Rui Neto Marinheiro, Fernando Brito e Abreu
TL;DR
The paper introduces a low‑cost, edge‑computing approach to monitor overtourism by sensing wireless traces from visitors’ mobile devices. It formalizes the STToolkit architecture, enabling SME deployment with edge data collection, anonymization, and cloud‑based visualization, while offering flexible uplink options via Wi‑Fi or LoRaWAN to address connectivity constraints. A Wi‑Fi detection algorithm is described to counter MAC address randomization through a combination of data‑frame analysis and IE fingerprinting to produce near real‑time crowding signals. Field validation on Iscte campus demonstrates the system’s adaptability across indoor and outdoor hotspots, with dashboards, heatmaps, and BIM‑based visualizations to support just‑in‑time mitigation actions. The work highlights a practical, scalable pathway toward smarter, residents‑friendly tourism management through open‑source tools and SME‑oriented deployment guidance, with future plans for broader validation and site deployments.
Abstract
Overtourism occurs when the number of tourists exceeds the carrying capacity of a destination, leading to negative impacts on the environment, culture, and quality of life for residents. By monitoring overtourism, destination managers can identify areas of concern and implement measures to mitigate the negative impacts of tourism while promoting smarter tourism practices. This can help ensure that tourism benefits both visitors and residents while preserving the natural and cultural resources that make these destinations so appealing. This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. A flexible architecture was designed for a smart tourism toolkit to be used by Small and Medium-sized Enterprises (SMEs) in crowding management solutions, to build better tourism services, improve efficiency and sustainability, and reduce the overwhelming feeling of pressure in critical hotspots. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies, mitigating the effect of MAC address randomization. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database, without infringing privacy rights. After that edge computing, sensors communicate the crowding information to a cloud server, by using a variety of uplink techniques to mitigate local connectivity limitations, something that has been often disregarded in alternative approaches. Field validation of sensors has been performed on Iscte's campus. Preliminary results show that these sensors can be deployed in multiple scenarios and provide a diversity of spatio-temporal crowding data that can scaffold tourism overcrowding management strategies.