Effects of transport-related COVID-19 policy measures: A case study of six developed countries

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

COVID-19 control measures unexpectedly increased the duration of stay at High Speed Rail stations during the first community outbreak in Taiwan

During the COVID-19 pandemic, Taiwan has implemented strict border controls and community spread prevention measures. As part of these efforts, the government also implemented measures for public transportation. In Taiwan, there are two primary public transportation systems: Taiwan Railways (TR) is commonly utilized for local travel, while the Taiwan High-Speed Rail (THSR) is preferred for business trips and long-distance journeys due to its higher speed. In this study, we examined the impact of these disease prevention measures on the number of passengers and duration of stay in two major public transportation systems during the first community outbreak from April 29th to May 29th, 2021. Using data from a local telecommunications company, our study observed an expected decrease in the number of passengers after the cancellation of non-reserved seats at both TR and THSR stations across all 19 cities in the main island of Taiwan. Surprisingly, however, the duration of stay in some of the cities unexpectedly increased, especially at THSR stations. This unanticipated rise in the duration of stay has the potential to elevate contact probability among passengers and, consequently, the transmission rate. Our analysis shows that intervention policies may result in unforeseen outcomes, highlighting the crucial role of human mobility data as a real-time reference for policymakers. It enables them to monitor the impact of disease prevention measures and facilitates informed, data-driven decision-making.

Interaction between travel restriction policies and the spread of COVID-19

To investigate the interaction between travel restriction policies and the spread of COVID-19, we collected data on human mobility trends, population density, Gross Domestic Product (GDP) per capita, daily new confirmed cases (or deaths), and the total confirmed cases (or deaths), as well as governmental travel restriction policies from 33 countries. The data collection period was from April 2020 to February 2022, resulting in 24,090 data points. We then developed a structural causal model to describe the causal relationship between these variables. Using the Dowhy method to solve the developed model, we found several significant results that passed the refutation test. Specifically, travel restriction policies played an important role in slowing the spread of COVID-19 until May 2021. International travel controls and school closures had an impact on reducing the spread of the pandemic beyond the impact of travel restrictions. Additionally, May 2021 marked a turning point in the spread of COVID-19 as it became more infectious, but the mortality rate gradually decreased. The impact of travel restriction policies on human mobility and the pandemic diminished over time. Overall, the cancellation of public events and restrictions on public gatherings were more effective than other travel restriction policies. Our findings provide insights into the effects of travel restriction policies and travel behavioral changes on the spread of COVID-19, while controlling for informational and other confounding variables. This experience can be applied in the future to respond to emergent infectious diseases.

Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11116-023-10392-2.

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

How do the smart travel ban policy and intercity travel pattern affect COVID-19 trends? Lessons learned from Iran

COVID-19, as the most significant epidemic of the century, infected 467 million people and took the lives of more than 6 million individuals as of March 19, 2022. Due to the rapid transmission of the disease and the lack of definitive treatment, countries have employed nonpharmaceutical interventions. This study aimed to investigate the effectiveness of the smart travel ban policy, which has been implemented for non-commercial vehicles in the intercity highways of Iran since November 21, 2020. The other goal was to suggest efficient COVID-19 forecasting tools and to examine the association of intercity travel patterns and COVID-19 trends in Iran. To this end, weekly confirmed cases and deaths due to COVID-19 and the intercity traffic flow reported by loop detectors were aggregated at the country's level. The Box-Jenkins methodology was employed to evaluate the policy's effectiveness, using the interrupted time series analysis. The results indicated that the autoregressive integrated moving average with explanatory variable (ARIMAX) model outperformed the univariate ARIMA model in predicting the disease trends based on the MAPE criterion. The weekly intercity traffic and its lagged variables were entered as covariates in both models of the disease cases and deaths. The results indicated that the weekly intercity traffic increases the new weekly COVID-19 cases and deaths with a time lag of two and five weeks, respectively. Besides, the interrupted time series analysis indicated that the smart travel ban policy had decreased intercity travel by around 29%. Nonetheless, it had no significant direct effect on COVID-19 trends. This study suggests that the travel ban policy would not be efficient lonely unless it is coupled with active measures and adherence to health protocols by the people.

Build Healthier: Post-COVID-19 Urban Requirements for Healthy and Sustainable Living

The COVID-19 pandemic has brought a renewed interest in urban environment and healthy living and the changes in urban environments which can make for a healthier living. Today, more than 50% of the global population lives in urban areas, and in Europe the number is 75%. We present a narrative review to explore considerations and necessary requirements to achieve health and well-being within strategies for healthy design and urban planning whilst rethinking urban spaces for a post-COVID-19 and carbon-neutral future. The achievement of health and well-being demands healthy design strategies, namely, (1) moving from the concept of infrastructure for processes to the infrastructure for healthy living—requirements for healthy places, cycling, walking, disintegrating the role of polluting traffic from the urban environments, social vulnerability and equality; (2) physical space that will achieve standards of ‘liveable communities’—open, green space requirements and standards for any built environment; (3) mainstreaming ‘in-the-walking distance’ cities and neighbourhoods for healthy physical activities for daily living; (4) exploring any of the new concepts that connect the nexus of urban spaces and public health and improving of the population’s well-being. Public health needs to be prioritised systematically in planning of built environments, energy generations, sustainable food production, and nutrition.

Estimating CO2 emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport

The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an "emergency supply emission estimation methodology" (ESEEM). The CO₂ emissions from the global air dispatch of COVID-19 vaccines were estimated using two hypothetical scenarios of one dose per capita and additional doses secured. The robustness of the model was tested with the Monte Carlo Simulation method (MCM) based one-sample t-test. The model was validated using the "Expression of Uncertainty in Measurement (GUM)" and GUM's MCM approaches. The results showed that to dispatch at least one dose of the COVID-19 vaccine to 7.8 billion people, nearly 8000 Boeing 747 flights will be needed, releasing approximately 8.1 ± 0.30 metric kilotons (kt) of CO₂. As countries secure additional doses, these figures will increase to 14,912 flights and about 15 ± 0.48 kt of CO₂. According to the variance-based sensitivity analysis, the total number of doses (population), technology, and wealth play a significant role in determining CO₂ emissions across nations. Thus, wealthy nations' long-term population reduction efforts, technological advancements, and mitigation efforts can benefit the environment as a whole and the CO₂ burdens associated with current COVID-19 and any future disasters' emergency-supply transportation.

The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing

The global closure policy to limit the spread of the new coronavirus (COVID-19) in 2020 was based on public safety and health considerations. In the implementation of arrangements to prevent the epidemic, the function of the transportation system as a basis for securing cities has been severely affected. After summarizing the domestic and international literature on epidemic policies and travel, this study analyzes the changes of the spatial and temporal distribution characteristics of people’s travel and the impacts in the context of the two epidemic phases in Beijing and abroad. During the epidemic, traffic volume into and out of Beijing showed a downward trend. In our study, we found that total travel volume in Beijing during the Spring Festival in 2020 was down by about 70% year-on-year, the distribution of daily traffic trips during the day was not affected by the outbreak, and six urban areas in the center of Beijing experienced greater declines in travel volume compared to other urban areas. The conclusions of the study can provide a reference for the sustainability and recovery of urban areas and formulation of policies in the subsequent pandemic era in terms of the relationship between public travel and epidemic control.

Statistical indicators of compliance with anti-COVID-19 public health measures at European airports

International travel and the infrastructures involved are key elements in controlling and predicting the number of infections by an infectious disease (specifically COVID-19 cases). This research presents the rates or percentages of compliance with COVID-19 mitigation measures at several international airports in Europe (Madrid, Dublin, Paris-Charles de Gaulle, Zurich, Barcelona, and Bilbao). A structured survey called the COVID-19 Measures Implementation Rate at Airports (MIRA) was developed. First, the validity and reliability of the measurements obtained by MIRA were analyzed. A total of 1239 volunteers (passengers, cabin crew, and ground crew) participated in the study and answered the MIRA questionnaire. Second, once the validity and reliability of the measurements were assured, the rates or percentages of cases that observed compliance with the mitigation measures were calculated. The results indicated that participants perceived a low degree of compliance with sanitary measures in their international travel (the proportions ranged from 52.6% to 59%). The airports with the highest compliance with mitigation measures were the Dublin (with a rate of 70%) and Zurich airports (with a rate of 69.1%). In conclusion, the percentages could be low due to the ineffective implementation of some of the mitigation measures. The low percentages are not related to the health measures themselves. The implications of mitigation measures for containing the transmission of infectious diseases such as COVID-19 are discussed.

The economic impacts of traffic consumption during the COVID-19 pandemic in China: A CGE analysis

The transportation sector has played an important role during the COVID-19 pandemic. Like many industries, it experienced a sharp decline during the pandemic. The reduced traffic consumption has been caused by objective conditions, such as traffic control measures, and subjective factors, such as the perception of the COVID-19 pandemic. This study uses the computable general equilibrium (CGE) model to examine the economic impacts of traffic consumption during the COVID-19 pandemic in China. Moreover, to evaluate the impact of the government's economic stimulus policy related to transportation, this study examines the policy effects of transportation investment. This study suggests that, first, China's macroeconomy has been severely affected by reduced traffic consumption. The period when the pandemic was most severe had the largest GDP decrease (0.49%). Second, transportation consumption is closely associated with the output of all industries. As the pandemic worsens, the output of all sectors declines more. Of the transport sectors, road transport has the largest output decrease (10.17%), followed by railway (1.76%) and air sectors (1.53%). The service industry is the most negatively affected among the non-transportation sectors. Finally, transportation infrastructure investment can effectively promote the economy and create jobs. In addition, railway investment plays a more positive role in the economy than road and air transports. The findings provide a detailed understanding of the economic impact of the significantly reduced traffic consumption at different stages of the pandemic.