Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model.

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

How do customers change their purchasing behaviors during the COVID-19 pandemic?

This study focuses on examining how customers' shopping behaviors have changed during the pandemic and contributing variables. Three primary shopping modes include online purchases, curbside pickup, and in-store shopping. The dependent variables are the changes in customers' spending in those three modes during the pandemic. The theory of fear appeal was used as the theoretical foundation for selecting independent variables. Based on this theory, two groups of independent variables were identified, fears for health and fears for financial conditions due to COVID-19. Additionally, demographic variables were also included in the analysis. The data from Census Bureau's Household Pulse Survey Phase 3.1 collected from June 23 to July 5, 2021, was used with 24,998 useable cases. Logistic regression was used to analyze the data to test the effects of independent variables on customers' shopping behavior changes in the three modes. The results show that both fears for health and fears for financial conditions have effects on the shopping behavioral changes. Due to those fears, residents change their shopping behaviors by considering the shopping modes that allow them to deal with or avoid the risks. Additionally, demographic variables, including age, gender, race, income, and marriage status, also have significant impacts on their shopping decisions.

Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation.

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.

A qualitative analysis of social and emotional perspectives of airline passengers during the COVID-19 pandemic.

Background: Fear of illness, economic damage, and stigma have had a devastating impact on the travel industry and have caused a significant reduction in both business and leisure travel. This study examines passengers' social and emotional perspectives during and after the COVID-19 pandemic, building on a prior quantitative study that identified factors that predict a person's willingness to fly during the COVID-19 pandemic. Methods: This study used a qualitative method with a phenomenological perspective and hermeneutic design. Fifteen adults from the United States participated in a personal interview designed to capture demographics, individual safety measures, feelings, and concerns involving air travel during the pandemic. Personal interview transcripts were then inspected by the researchers using a constant comparison method. Results: The personal experiences of participants were dominated by projections of trust issues and emotional heuristics, protective behaviors, and fear of confrontations with others, and a fear of the unknown. These themes emerged even in participants who continued to fly during the pandemic. Conclusion: Insights into travelers' emotions, trust, and fears may help airlines and other segments of the travel industry to develop targeted messaging that supports the trust and safety issues confronted by frequent travelers.

Projecting daily travel behavior by distance during the pandemic and the spread of COVID-19 infections - Are we in a closed loop scenario?

Understanding the future development of COVID-19 is the key to contain the spreading of the coronavirus. The purpose of this paper is to explore a potential relationship between United States residents' daily trips by distance and the COVID-19 infections in the near future. The study used the daily travel data from the Bureau of Transportation Statistics (BTS) and the COVID-19 data from the Centers for Disease Control and Prevention (CDC) in the United States. Time-series forecast models using Autoregressive Moving Average (ARIMA) method were constructed to project future trends of United States residents' daily trips by distance at the national level from November 30, 2020, to February 28, 2021. A comparative trend analysis was conducted to detect the patterns of daily trips and the spread of COVID-19 during that period. The results revealed a closed loop scenario, in which the residents' travel behavior dynamically changes based on their risk perception of COVID-19 in an infinite loop. A detected lag in the travel behavior between short trips and long trips further worsens the situation and creates more difficulties in finding an effective solution to break the loop. The study shed new light on efforts to contain and control the spread of the coronavirus. The loop can only be broken with proper and prompt mitigation strategies to reduce the burden on hospitals and healthcare systems and save more lives.