Current Issue

The social changes triggered by the COVID-19 pandemic are not transient phenomena. Historical pandemics such as SARS, MERS, Ebola, Avian Influenza (bird flu), and H1N1 flu (swine flu) indicate that COVID-19 will not be humanity’s last encounter with pandemics (Gill, 2020). Experts anticipate recurring pandemics throughout history and observe that the pandemic cycle is gradually shortening (Gill, 2020). Moreover, the COVID-19 pandemic has persisted for an extended period due to subvariant mutations. Variants such as Alpha, Delta, and Omicron have emerged, further extending the pandemic beyond expectations. This enduring pandemic era necessitates humanity’s adaptation to pandemic lifestyles. To cope with this prolonged pandemic era, countries worldwide, including the South Korea, have reviewed and implemented “With COVID-19” policies, enabling coexistence with the virus rather than waiting for its end. Therefore, adapting and coexisting with pandemics is imperative. To achieve this, it is essential to anticipate and prepare for changes in our daily lives during pandemic eras based on a thorough analysis of past experiences.

The COVID-19 pandemic has caused significant changes in traffic patterns. Many studies have reported a decrease in public traffic and traffic volume due to COVID-19. Specifically, some studies have focused on the reduction in public transportation usage (Jang, 2021; Lee et al., 2021), analyzed changes in transportation habits (Cho et al., 2020), and examined the correlation between traffic volume and the number of confirmed COVID-19 cases. However, further research is necessary to understand traffic patterns between regions and movements related to tourism.

Moreover, traffic patterns are a crucial factor influencing a nation’s industrial structure. For example, traffic restrictions have led to the revitalization of the delivery industry, while the dine-in restaurant industry has experienced a decline due to increased food delivery services. Analyzing traffic patterns provides insights into overall changes in our daily lives.

Therefore, this study aims to demonstrate the common characteristics of human movement patterns during the pandemic by comparing the centrality of South Korean cities(tollgates) before and after the pandemic. The study will utilize highway traffic data to measure regional centrality before and after COVID-19 and analyze changes in tourism patterns in the post-pandemic era. By comparing changes in regional centrality, the study seeks to identify common characteristics of regions where regional centrality increases due to COVID-19. This study will contribute to the establishment of national transportation policies by enabling the prediction of changing traffic patterns in the post-pandemic era. Additionally, analyzing tourism-related traffic is meaningful from a regional development and policy perspective as it allows local governments, tourism industry workers, and small business owners to identify competitive tourism spots in the post-pandemic era and revitalize the tourism industry.

This study utilizes the social network analysis (SNA) method to examine changes in the influence of regions before and after the COVID-19 pandemic. SNA is widely employed in various fields, including organizational sociology, political science, organization theory, and strategy, as it allows for the analysis of different elements within social networks, such as relationships between individuals or web search keywords (Zaheer et al., 2010). The fundamental principle of SNA is to represent each element of a social network as dots and lines based on graph theory. The relationships between these dots in the graph are then interpreted using indicators such as density, inclusion, and centrality. Network analysis provides a structural perspective that is particularly useful in tourism studies (Scott et al., 2008).

Social Network Analysis is a commonly utilized research method in the field of tourism due to its capacity to examine different components of a network and their connections, making it a critical tool for tourism research (Scott et al., 2009). For example, Hong et al. (2015) employed degree centrality, eigenvector centrality, and closeness centrality to explore the correlation between destination centrality and tourism flows. Similarly, Park et al. (2016) utilized social network analysis to investigate the multiple destination tourism patterns of visitors to Gyeonggi-do in South Korea, using degree centrality and eigenvector centrality to understand tourism patterns beyond simple frequency comparison. Additionally, Liu et al. (2017) conducted a network analysis to examine the tourism mechanism in Xinjiang, China, and identified a relationship between three types of proximities (region proximity, grade proximity, and tenure proximity) and the tour attraction network. Despite the wide range of network analysis research in this field, further investigation is needed regarding tourism destinations (Liu et al., 2017).

Public health issues, war, terrorism, and social conflict have a profound impact on tourism (Qiu et al., 2020). Analyzing the consequences of these impacts can help in developing future strategies. The outbreak of COVID-19 in 2020 had a significant negative impact on global consumer markets and international tourism demand (UNWTO, 2020). Tourism is particularly vulnerable to pandemics like COVID-19 because it is highly sensitive to people’s movements (Gössling et al., 2021). Concerns about contagion reduce tourism demand and increase the preference for destinations with lower population density (Everingham and Chassagne, 2020). Jin et al. (2019) conducted research on the impact of pandemics on tourists in China (Jin et al., 2019). Furthermore, a study that conducted network analysis on tourism patterns in Gangwon-do, Korea, confirmed that post-pandemic travel routes were simplified and focused on beach destinations (Jeon and Yang, 2021). Since COVID-19 may not be the last pandemic (Gill, 2020), it is crucial to conduct further research to predict tourists’ behavior during pandemics, as current research remains insufficient (Jeon and Yang, 2021).

Previous studies on traffic demand prediction in Korea have primarily focused on weekdays and commuting (Chu et al., 2007), with studies on tourism factors considering only personal or psychological factors such as age, gender, occupation, income, and residence (Kim et al., 2004; Lee and Choi, 2020; No et al., 1994). Environmental factors, such as politics, economy, society, and culture, which can significantly impact the supply side of the tourism industry, have been largely neglected in previous studies (Kim, 1990). However, the COVID-19 pandemic has emerged as a crucial environmental factor that affects society’s tourism demand. Although some studies have analyzed actual traffic data to examine traffic patterns (Chu et al., 2007), they have failed to consider the pandemic’s impact on tourism-purpose traffic. Furthermore, the studies conducted after the pandemic have not yet analyzed the national scope (Jeong et al., 2023) or examined the ‘pattern’ of traffic (Bhin et al., 2021; Cho et al., 2020; Jang, 2021; Lee, et al., 2021; Lee, S. et al., 2020), or not used transportation data as scientific rationale (Mogaji, 2020). Therefore, this study aims to investigate the differences in the network by analyzing tourism-purpose traffic before and after the pandemic.

This study delved into the evolving landscape of tourism behavior in the post-pandemic era, leveraging Origin-Destination (O-D) data from South Korea spanning the years 2019, 2020, and 2021. Employing Social Network Analysis, specifically focusing degree and eigenvector centrality, we unearthed three pivotal characteristics shaping the post-pandemic tourism paradigm.

First and foremost, our investigation illuminated a noteworthy shift in tourist preferences, marked by an uptick in the allure of local provinces as favored destinations. This trend coincided with a waning centrality of the Seoul metropolitan area. However, intriguingly, as the post-pandemic era unfolded, the centrality of the Seoul metropolitan area began to regain its vigor. This dynamic shift underscores the fluidity of tourist choices in the wake of global health crises. Secondly, we observed a fluctuation in tollgate centralities during the pivotal years of 2019, 2020, and 2021. Between 2019 and 2020, many tollgate centralities dipped, suggesting a relaxation in individuals’ vigilance toward the virus. However, between 2020 and 2021, an opposing trend emerged, with numerous tollgate centralities experiencing an uptick. This shift hints at the nuanced changes in public attitudes toward infection risk over the course of the pandemic. Lastly, our research highlighted the emergence of new and unconventional tourist destinations in the post-pandemic era. This development can be attributed to the increasing demand for natural sites with minimal human interaction. The trend underscores the resilience and adaptability of the tourism industry in response to evolving health concerns.

The implications of these findings are profound. They offer valuable insights for predicting and preparing for future shifts in tourism behavior in the face of recurring pandemics. Policymakers can use this research to inform urban development strategies that are attuned to the changing dynamics of tourism. This underscores the essential role of tourism research in shaping and guiding the formulation of future urban policies.

In conclusion, our study not only sheds light on the transformative nature of post-pandemic tourism but also underscores the indispensable role of academic research in shaping resilient and adaptive urban policies. As we navigate a world marked by ongoing uncertainties, understanding the evolving patterns of tourism behavior remains crucial for fostering sustainable and resilient cities in the future.

This research has several limitations. The study relies on toll collection system (TCS) data, which may not capture all aspects of tourism behavior, such as non-motorized travel or movements without passing tollgates. It may also include movements for non-tourism purposes. It is also specific to South Korea, and the findings may not be generalizable to other regions or countries with different socio-cultural and geographical contexts. Moreover, the analysis is based on data from 2019, 2020, and 2021, covering a relatively short period of time. A longer time frame could provide a more comprehensive understanding of post-pandemic tourism trends. Therefore, future studies will be conducted over an extended period, utilizing multiple datasets to comprehend tourist mobility patterns more thoroughly.