Full length article
Crowd management COVID-19

https://doi.org/10.1016/j.arcontrol.2021.04.006Get rights and content

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.

Keywords

COVID-19
Agent-based model
POI recommendation
Mitigation strategy

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Liliana Duran-Polanco is a Computer Systems Engineer, she has worked as a software developer and currently is pursuing her M.Sc. at Cinvestav Guadalajara. Her research focuses on decision-making under uncertainty in IoT systems.

Email: [email protected]

Dr. Siller is a Principal Investigator at Cinvestav Guadalajara, member of the Computer Science and Telecommunications Research Groups. During the period 2015-2016 he was Visiting Associate Professor at the MIT Media Lab and a Fulbright-García Robles Research Fellow. During his academic visit, he worked in the City Science area studying the emerging and aggregate complex behavior of cities, from the perspective of Complex Systems Theory. His research areas include City Science, Analysis and Modeling of Networked Systems, Distributed Systems, Computational Intelligence, and IoT in application domains such as Smart Cities, Health, Blockchain, Industry 4.0, Intelligent Transport Systems and Urban Agriculture.

Twitter: mario_siller

Linkedin: https://www.linkedin.com/in/mario-siller

Email: [email protected]/[email protected]

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