Crowd management COVID-19

Abstract

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic

“…During the COVID-19 pandemic, apps were designed to determine the density of people at a certain point. 12 However, cell phone reception at a nature-based leisure travel destination may be a problem, unlike in other types of leisure travel destinations. 13 Nevertheless, similar techniques are still helpful for this purpose.…”

Crowd dynamics research in the era of Covid-19 pandemic: Challenges and opportunities

“…Clearly, developments of such systems, in addition to their requirements for optimisation algorithms on the management side, introduces issues such as end-user acceptance and experience. This may itself require behavioural research as well as behavioural models that can replicate acceptance level and interaction of end-users with such systems ( Durán-Polanco and Siller, 2021 , Yang et al, 2021 ).…”

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

“…From the point of view of mobility and route planning, disease control influences the way such algorithms are defining the routes, e.g., by indicating different routes for pedestrians inside buildings (an entry route and a different exit route) or by implementing pedestrian distancing and crowd control. For example, Durán-Polanco and Siller proposed a recommendation system using a behavioral game theory and agent-based models [38]. Geneletti et al simulated policy scenarios for different restrictions, to assess potential crowding of green areas [39].…”