Covid-19 risk assessment in public transport using ambient sensor data and wireless communications

Abstract

Covid-19 causes one of the most alarming global health and economic crises in modern times. Countries around the world establish different preventing measures to stop or control Covid-19 spread. The goal of this paper is to present methods for the evaluation of indoor air quality in public transport to assess the risk of contracting Covid19. The first part of the paper involves investigating the relationship between Covid-19 and various factors affecting indoor air quality. The focus of this paper relies on exploring existing methods to estimate the number of occupants in public transport. It is known that increased occupancy rate increases the possibility of contamination as well as indoor carbon dioxide concentration. Wireless data collection schemes will be defined that can collect data from public transportation. Collected data are envisioned to be stored in the cloud for data analytics. We will present novel methods to analyze the collected data by considering the historical data and estimate the virus contagion risk level for each public transportation vehicle in service. The methodology is expected to be applicable for other airborne diseases as well. Real-time risk levels of public transportation vehicles will be available through a mobile application so that people can choose their mode of transportation accordingly.

How Internet of Things responds to the COVID-19 pandemic

“… Allam & Jones (2020) emphasized the critical necessity to standardize the protocols for improved smart city communication and the need to democratize the smart city technology sector to foster equality and transparency among stakeholders, which allows for more collaboration against disasters, in general, and COVID-19, in particular. Senturk et al. (2020) used cloud computing techniques to estimate the number of persons which causes pollution in the transportation.…”

Addressing the Challenges of COVID-19 Social Distancing Through Passive Wi-Fi and Ubiquitous Analytics: A Real World Deployment

“…The motivations of many of these studies are diverse, some look at energy waste on scanning methods [3], realistic facility management and planning [37], while others looked at crowding factors, flock detection and waiting times, speed and frequent paths [21] and even social information like popularity of events (in the case of [4] singers in concerts). In the pandemic context, Wi-Fi has also been used to detect the contagion risk in public transport [10] by using multiple sensors that monitor the air quality, temperature humidity and use Wi-Fi probes to detect the people present thus creating an index for contagion risk. Another solution that employs Wi-Fi probe requests is presented in [42] with the goal to estimate indoor crowd densities based on dynamic fingerprints based on signal strength and proposing a crowd density estimation solution based on the positioning algorithm.…”