Traffic Impacts of the COVID-19 Pandemic: Statewide Analysis of Social Separation and Activity Restriction

The COVID-19 pandemic resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on travel behavior have also been sudden and wide ranging. This study describes the drastic changes in human behavior using the analysis of highway volume data as a representation of personal activity and interaction. Same-day traffic volumes for 2019 and 2020 across Florida were analyzed to identify spatial and temporal changes in behavior resulting from the disease or fear of it and statewide directives to limit person-to-person interaction. Compared to similar days in 2019, overall statewide traffic volume dropped by 47.5%. Although decreases were evident across the state, there were also differences between rural and urban areas and between highways and arterials both in terms of the timing and extent. The data and analyses help to demonstrate the early impacts of the pandemic and may be useful for operational and strategic planning of recovery efforts and for dealing with future pandemics.

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning

While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19, and 2) estimate a model to predict the change of grocery store visits in the future, within the same phase of the pandemic. The study period (February 15–May 31, 2020) covered the outbreak and phase-one re-opening. Six counties/states in the United States were examined. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on March 13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May, but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With data from Google Mobility Reports, this study used a long short-term memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning.

While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19, and 2) estimate a model to predict the change of grocery store visits in the future, within the same phase of the pandemic. The study period (February 15-May 31, 2020) covered the outbreak and phase-one re-opening. Six counties/states in the United States were examined. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on March 13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May, but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With data from Google Mobility Reports, this study used a long short-term memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

Investigating COVID-19 Induced Taxi and For-Hire Vehicle Ridership Disparities

The impacts of COVID-19 on for-hire vehicle (FHV) (e.g., Uber/Lyft, often referred to as transportation network companies in other locations) and taxi use have been relatively understudied compared with transit and personal vehicles. This study analyzed and estimated the changes in ridership for taxis and FHVs in New York City during the COVID-19 pandemic to determine whether it had disproportional impacts on these competing modes, how these impacts varied over time and space, and the associated factors. Data supporting the analyses came from the Taxi and Limousine Commission, the COVID-19 Data Repository, Google's Community Mobility Reports, the American Community Survey, and the Primary Land Use Tax Lot Output. Temporal change was measured by the daily taxi/FHV ridership deviation from a defined baseline, which showed that COVID-19 more negatively impacted taxis than FHVs. Temporal moving average models were then employed, which showed that COVID-19 had different temporal impacts on taxis and FHVs in relation to the parameters’ significance, magnitude, and temporal correlation patterns. In general, taxi/FHV ridership dropped when people spent more time at home and the number of confirmed COVID-19 cases was greater. The spatial variation in taxi/FHV ridership was measured by the coefficient of variation. Spatial regression models indicated that the land use of a zone affected taxi/FHV ridership during the pandemic. In addition, a zone with more carless/car-free households, older persons, or more children enrolled in school was more likely to experience a decrease in taxi/FHV ridership. A zone with more workers who commuted by walking or taking transit (excluding taxis) in pre-COVID times was more likely to see a decrease in taxi/FHV ridership. A zone with more people working from home pre-COVID, was more likely to see an increase in FHV ridership. The models showed that COVID-19 had greater spatial impacts on taxis than FHVs. Based on these results, this study provides insights as to what factors affected ridership of the two competing travel modes and suggests actions that transportation authorities could take to reduce temporal and spatial impact disparities.

How do Parents Manage Symptomatic Children? Social-Distancing Insights for COVID-19 and Seasonal Influenza.

Strategies for controlling pandemics include social distancing. Using data from a 2016 nation-wide survey pertaining to influenza, (generalized) ordered logit models are developed to identify the factors associated with the relative frequency (never/sometimes/always) a household (a) isolates a sick child from others in the household, (b) keeps the sick child out of school/daycare, (c) stops the child's social activities, (d) has a parent stay home to care for the child, and (e) has another adult care for the child. Marital status is non-significant for isolation practices but is significant in caregiving. Married individuals are 25% more likely to report a parent always staying home with a sick child. Males are more likely to report never isolating a sick child (6%, 3%, and 2% for actions a, b, and c, respectively) and 3% more likely to never have a parent stay home. Individuals knowledgeable about the disease are 10% more likely to always keep a sick child home from school/daycare. Parents are 27% more likely to always stay home with an infant. Individuals who had never worn masks (before the survey) are less likely to isolate a child within the household, but do not act significantly differently with respect to school/daycare.

Multistate Assessment of Roadway Travel, Social Separation, and COVID-19 Cases

This research was undertaken to comparatively assess the unprecedented travel and activity conditions related to the onset of coronavirus disease of 2019 (COVID-19) in the US in the first half of 2020. In this effort, roadway traffic volumes were used to relate government directives for social separation and COVID-19 case progression in ten diversely populated and located states. Among the key contributions of the research were its illustration of the amount and time scale of public response to activity restrictions across the country and the general finding that overall, governmental directives, as reflected in rapid traffic decreases, likely served their purpose. Another key finding was that by June 1st, no state had completely returned to routine levels of travel. Combined, the results of this study illustrate the effect of governmental action with respect to the course of the virus, including how varied timings of responses reflected outcomes based on the levels of threat and characteristics of individual locations. It is expected that this paper will be of use to practitioners, governmental, and researchers to assess and develop plans for future similar major events and emergencies. [ABSTRACT FROM AUTHOR] Copyright of Journal of Transportation. Part A: Systems is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influenza risk perception and travel-related health protection behavior in the US: Insights for the aftermath of the COVID-19 outbreak.

Influenza is a contagious virus affecting both one's health and economic productivity. This study evaluates uses a survey of 2168 individuals across the U.S. Ordered logit regressions are used to model risk perception and generalized ordered logit regressions are used to model risk mitigation travel-related decisions. Models are estimated for three influenza outbreak scenarios, specifically an individual's travel-related: 1) risk perceptions, 2) risk mitigation decisions when infected and the individual wants to prevent spreading it, and may want treatment, and 3) risk mitigation decisions when not infected and the individual wants to reduce exposure. Risk perception results show that a recent personal experience with influenza-like symptoms and being female significantly increased risk perception at mandatory and medical trip locations. Risk mitigation model results show that males are less likely to alter their travel patterns in response to the possible spreading of the virus or increasing exposure. Knowing the difference between influenza and the stomach flu is more influential in reducing travel than a recent influenza experience in one's household. Individuals proactive with their health (i.e., receive the vaccine, have health insurance) are also proactive in seeking medical attention and reducing influenza spread. Lastly, aligned with the Protection Motivation Theory, individuals reduce travel to locations in which they perceived medium or high risk. However, increased risk perceived at one's work location did not significantly reduce travel. The findings provide insight into the risk perception and mitigation behavior of the American public during the COVID-19 pandemic and after restrictions are lifted.

Traffic Impacts of the COVID-19 Pandemic: Statewide Analysis of Social Separation and Activity Restriction

The COVID-19 pandemic resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on travel behavior have also been sudden and wide ranging. This study describes the drastic changes in human behavior using the analysis of highway volume data as a representation of personal activity and interaction. Same-day traffic volumes for 2019 and 2020 across Florida were analyzed to identify spatial and temporal changes in behavior resulting from the disease or fear of it and statewide directives to limit person-to-person interaction. Compared to similar days in 2019, overall statewide traffic volume dropped by 47.5%. Although decreases were evident across the state, there were also differences between rural and urban areas and between highways and arterials both in terms of the timing and extent. The data and analyses help to demonstrate the early impacts of the pandemic and may be useful for operational and strategic planning of recovery efforts and for dealing with future pandemics.