Wireless Crowd Detection for Smart Overtourism Mitigation

TL;DR

This paper presents a low-cost, wireless-based crowd detection system for smart overtourism mitigation, enabling real-time monitoring and management of tourist hotspots to reduce negative impacts.

Contribution

It introduces a flexible architecture for crowd sensing using mobile device wireless activity, with privacy-preserving fingerprinting and edge-cloud communication for overtourism management.

Findings

Sensors effectively detect crowd density in various scenarios.

Preliminary validation shows accurate spatio-temporal crowd data collection.

System supports sustainable tourism practices by identifying overcrowded areas.

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.