Managing crowds is important not only during evacuation in disasters such as earthquakes and fires but also during normal situations. In particular, places where many people gather every day, such as stations or event venues, need such management to prevent crowd accidents. Moreover, efficient guidance that prevents people from waiting or queuing can improve facility services and lead to business opportunities. In this study, we propose a crowd management platform to prevent crowd accidents and provide efficient guidance to visitors. Specifically, we integrate real-time observations of crowd conditions, predictions, and risk assessments through simulation and crowd control in collaboration with security and facility managers. We also present the results of operating this platform in actual fields, which contribute to and support the safety and comfort of individuals.

In event management, preventing excessive overcrowding is not only essential for providing comfort but also crucial for ensuring safety. However, understanding the crowd dynamics of participants in outdoor events can be challenging. One of the primary reasons is the limited availability of sensing systems suitable for outdoor use. Challenges include the need for power outlets and adapting to dynamic environmental conditions and unclear event boundaries. Consequently, there is still uncertainty about which measurements can be conducted to scientifically manage crowding based on sound principles. Therefore, there is a need for systems that are capable of discerning spatial and temporal heterogeneity in density and accurately estimating the number of people in regions of interest in both sparse and congested areas. In this study, we propose a novel approach for measuring and understanding crowd states at outdoor events. We designed a highly portable measurement system utilizing Bluetooth technology to monitor crowd density in real time, ensuring uninterrupted data collection even in remote event locations. This system stands out for its ability to operate effectively under diverse weather and lighting conditions without power outlets, making it highly adaptable to various outdoor settings. In our experiments, conducted at four distinct outdoor event locations, we used a 360° camera and LiDARs to validate the system. For instance, we deployed the system at 40-m intervals in a shopping district during a high-density parade. This deployment enabled us to capture the movement of the crowd and estimate the total number of people within the district. A key finding was the system’s capability to detect temporal and spatial congestion in both sparse and crowded areas. The system’s potential to estimate crowd sizes and manage diverse outdoor events marks an advancement over traditional methods like cameras and LiDARs.