Changes in People’s Mobility Behavior in Greece after the COVID-19 Outbreak

Unraveling the causes of the Seoul Halloween crowd-crush disaster

As the world steadily recovers from the COVID-19 pandemic, managing large gatherings becomes a critical concern for ensuring crowd safety. The crowd-crush disaster in Seoul in 2022 highlights the need for effective predictive crowd management techniques. In this study, an empirical analysis of this incident is conducted using data from various sources, and model-based simulations are created to replicate hazardous crowd conditions in high-risk areas. In the empirical analysis, mobile device data indicates a significant increase in population above normal levels in the disaster area just hours before the incident occurred. In the simulations, a hydrodynamic model is employed to simulate a bidirectional collision, which quantitatively demonstrates that the average density during the crush reached 7.57 ped/m2 (with a maximum of (9.95)ped/m2). Additionally, the average crowd pressure peaked at 1,063 N/m (with a maximum of 1,961 N/m), and the maximum velocity entropy was 10.99. Based on these findings, it can be concluded that the primary causes of the disaster were the substantial population, bidirectional collision, and escalating panic. The results of controlled simulations under various management strategies are then presented. By implementing effective crowd management techniques, crowd safety can be enhanced through quantitative comparisons of these key indicators.

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the "cautious" group versus the "fearless" group. The "cautious" group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the "cautious" group with higher perceived susceptibility is more obedient to government policies. In contrast, the "fearless" group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

A Comparison in Travel Characteristics of Bike-Sharing between College Students and Office Workers Based on Theory of Planned Behavior

As an important component of bike-sharing (BS) users, the travel behavior of college students and office workers is important to the promotion of BS within Chinese cities. To explore the influencing factors for the behavioral intentions of BS, this paper makes a different analysis between the two groups. Based on the theory of planned behavior, and using environmental awareness as an extended variable, a BS travel intention model was developed. A total of 676 valid questionnaires were collected and analyzed from college students and office workers in Zhengzhou. The results indicate that attitude, subjective norms, perceived behavior control, and environmental awareness have a positive impact on the behavioral intentions of BS. However, the influence degree of each variable is different between the two groups. Perceived behavior control, including travel time, travel cost, and cycling difficulty, has the greatest impact on BS behavioral intention for college students. Meanwhile, subjective norms, including policy and media publicity, has the most significant impact on BS behavioral intention for office workers. The impact of environmental awareness on college students' use of BS is higher than that of office workers. We also found that undergraduates use BS more frequently than postgraduates. The findings provide the clear influence factors on behavioral intentions of BS between college students and office workers, that can help policy optimization in terms of bike-sharing systems, giving some suggestion for an approach devoted to deepen the individual-context interactions.

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

Introduction

Physical mobility is critical for the spread of infectious diseases in humans. However, few studies have conducted empirical investigations on the impact of pre-pandemic travel mobility patterns on the diffusion of coronavirus disease 2019 (COVID-19). Therefore, this study examines its impact at the city-county level on the diffusion by the wave period during the two-year pandemic in South Korea.

Methods

This study first employs factor analysis by using the travel origin-destination data by travel mode at the county level as of 2019 to derive pre-pandemic travel mobility patterns. Next, the study identifies how they had affected the diffusion of COVID-19 over time by employing the negative binomial regression models on confirmed COVID-19 cases for each wave, including the entire pandemic period.

Results

The study derived five pre-pandemic mobility patterns: 1) rail-oriented mobility, 2) intra-county bus-oriented mobility, 3) road-oriented mobility, 4) high-speed rail-oriented mobility, and 5) inter-county bus-oriented mobility. Among them, the biggest risk to the diffusion of COVID-19 was the rail-oriented mobility before the pandemic if controlling such measures as accessibility, physical environment, and demographic and socioeconomic indicators. In addition, the order of the magnitudes for the impact of pre-pandemic travel mobility factors on its spatial diffusion had not changed during experiencing the three different wave periods during the two-year pandemic in South Korea.

Conclusions

The study concludes that the rail-oriented travel mobility pattern before the pandemic could pose the greatest threat factor to the spatial spread of COVID-19 at any scale and time. Policymakers should develop strategies to prevent the spatial spread of COVID-19 by reducing human mobility for daily living in areas with strong rail mobility patterns formed before the pandemic.