Mobile-Based Crowd Monitoring and Management: Assessment and Improvement

Crowding is a troublesome phenomenon due to safety concerns, public health risks, social tensions, degraded quality of experience for visitors, infrastructure strain, environmental impact, among other reasons. Overcrowding management requires crowd monitoring, the main topic to be addressed in this PhD research plan. In particular, it focuses on developing a machine-learning crowd-monitoring approach based on the detection of mobile device’s trace elements from their wireless technologies, namely Wi-Fi and Bluetooth. The expected major challenges are coping with (i) devices’ address randomization to derive unified fingerprints in each observation period, (ii) the combination of counting obtained from diverse wireless technologies, and (iii) the combination of data from multiple sources. It also aims to rely on user contributions, to enhance the quality of detection, by developing a mobile application. The latter will also help with real-time management of overtourism, promoting mitigation actions. The research plan builds upon preliminary results on developing a Smart Tourism Toolkit to monitor crowding levels in real-time in the scope of the European RESETTING project.