Comparing COVID spread to mask-wearing rates in public transportation using AI detection

The spread of COVID-19 has resulted in several changes worldwide. In particular, border closures and economic stagnation have significantly affected societies. Although the implementation of preventive measures has improved the pandemic scenario in several countries, the effectiveness of vaccines has decreased with the emergence of mutant viruses. With this background, the use of masks is considered the best method for preventing the spread of the virus. Notably, public transportation is closely related to socioeconomic activities, and the spread of infectious diseases is more likely in closed, dense, and congested areas. Moreover, the probability of infection during public transportation also depends on the proportion of commuters wearing masks. Based on the closed-circuit television footage of various public transportation spaces, the number of mask wearers can be analysed using artificial intelligence deep learning, and the probability of COVID-19 spread can be predicted by determining the proportion of mask wearers among the commuters. With this background, in this study, the importance of masks in controlling the spread of the virus is confirmed. In conclusion, appropriate measures can be implemented by determining the probability of infection according to the mask-wearing rate in public transportation spaces. © 2023 ICE Publishing: All rights reserved.

Relational analysis between economic activities and mobility during the COVID

Coronavirus disease (COVID-19) has significantly affected daily lives since its declaration as a Pandemic in March 2020 by the WHO. Studies in Korea to interpret the relationship between COVID-19 and transportation show that there has been a decrease in public transportation use, and a significant decrease when a widespread infection occurs. In addition, COVID-19 has affected the commercial environment, particularly the number of people using commercial districts where the use of retail outlets decreased significantly. This study analyses the disease-vectors (spread factors) of COVID-19 in terms of transportation demand, the revitalisation of commercial districts, living populations, and socioeconomic indicators. It attempts to identify different infection factors for each district in Seoul using a causal analysis methodology PLS-SEM (Partial Least-Squares Structural Equation Modelling) such that COVID-19 can be managed continuously with the aim to provide a foundation for pre-emptive actions by adjusting or controlling specific influencing factors pertaining to infectious diseases. © 2023 ICE Publishing: All rights reserved.

Metro Network Operational Solutions for Connectivity Control based on Percolation Theory

Recent studies have applied the percolation theory to analyze the connectivity of networks in the transportation field. However, research was conducted in a manner that completely removed the function of nodes or links. There was a limit in that applying public transportation was difficult to guarantee the right to move the captive rider. In this study, penalties were imposed on public transportation nodes in the form of wait times to remove the function of node partially. Accordingly, the travel time of a network was calculated by optimal strategy assignment to reflect passenger behavior. When nodes were randomly penalized without transfer distinctions, there was a critical point of travel-time increase between cases with penalties of 50 and 60 nodes, respectively, and percolation was observed indirectly. A large and global effect of increased travel time was observed when the penalties were issued only to transfer stations. The application of a trip frequency weight increases the effect of penalties on medium- or short-timed trips. The results of this study can be used to establish quarantine policies for controlling public transportation networks. Furthermore, it is the first attempt at observing percolation by partially limiting its function in the form of node penalties in a public transportation network.

Changes in Shared Bicycle Usage by COVID-19

The Seoul Metropolitan Government is promoting and implementing eco-friendly transportation policies covering the revitalization of public transportation, establishment of green transportation promotion areas, and the expansion of shared-bicycle use. With the prevalence of the COVID-19 pandemic, cities worldwide have been paralyzed by lockdowns, the strict implementation of social-distancing policies, and the shutdown of different industry sectors. People's movement and traffic volume have decreased significantly. The pandemic has forced to people change their travel-related decision-making tendencies. Against this backdrop, this study analysed the changes in shared-bicycle traffic in Seoul during the pandemic. This study analyzed the relationship between social distancing levels and shared bicycle usage using regression analysis. This study conducted an analysis of spatially formed clusters through Moran's I test and hotspot analysis. The results indicate that an important correlation between the spread of COVID-19 and shared bicycle use has been found. When social distancing levels increased by one level, the number of shared bicycle rental per day increased by about 10,000 times. As a result of spatial analysis, changes in shared bicycle usage are spatially forming clusters, and Moran's I test showed statistically significant results with a p-value very close to zero. The study results indicate that under special circumstances, government policy plays an important role in promoting green transportation. The use of green transportation is directly related to falling atmospheric pollution in the city. Such transportation can significantly benefit the environment. The government can produce more effective results by considering local characteristics to revitalize eco-friendly transportation. © 2021, AIDIC Servizi S.r.l.