With a perfect storm of existing capacity limits, rapid demand increases, and the labor shortages from initial COVID-19 pandemic reductions, container ports are facing major challenges in moving cargo. Since late 2020, U.S. container ports have been successfully bringing in goods at a much stronger pace than before the pandemic. This Data Spotlight includes: 20-Foot Equivalent Units (TEUs) Handled by the Top 10 U.S. Container Ports, January 2019-September 2021; and Number of Container Cranes at the Top 25 Container Ports, 2020.
One impact of the COVID-19 pandemic has been stricter crossing restrictions at international borders. This affects freight, workers, and tourism. In the U.S., incoming crossings were limited to freight and essential travel, which caused a sharp decline in the number of vehicles and pedestrians entering the U.S. from Canada and Mexico. The number of crossings gradually began to increase in 2021 when some restrictions eased. The number of personal vehicles entering the U.S. from Canada (4.4M) in 2021 decreased 83.4% from 2019 while pedestrian crossings (37,459) fell 92.8%. At the border with Mexico, the decrease was milder, with personal vehicles entering the U.S. (58.5M) down 19.9% and pedestrian crossings (27.9M) down 43.2%.
America’s land borders with Canada and Mexico are the busiest and most economically vital conduits for North American supply chains, with about $3 billion in daily cross-border trade. Before the pandemic, in 2019, the U.S. had $1.2 trillion in trade with Canada and Mexico. The total weight of that trade was 579.4 million tons. Although freight traffic was not affected by pandemic-related border closures, overall economic activity slowed in 2020 following pandemic lockdowns in all three nations. From 2019 to 2020, the value of total trade with Canada and Mexico declined by 13.3 percent to $1 trillion. The weight of goods crossing the border decreased 3.7 percent to 557.7 million tons. In 2022, as the pandemic waned and economic activity recovered, the value of U.S. trade with Canada and Mexico hit $1.6 trillion, up 50 percent from 2020 and 25 percent from pre-pandemic 2019.
Most but not all docked bikeshare systems experienced an increase in ridership in 2023. In 2023, ridership declined 3% on BlueBikes (serving the Boston, MA metro area) and 2% on Bay Wheels (serving the San Francisco, CA area). With the exception of Bay Wheels, growth on BlueBikes in years prior to 2023 more than offset the decline experienced during 2020, when COVID-19 significantly reduced ridership on nearly all systems. Ridership on Bay Wheels, on the other hand, was 1% percentage point below the 2019 level in 2023.
The COVID-19 pandemic caused unprecedented disruptions to human mobility and transportation systems worldwide, significantly altering travel behavior and mode choices. This study investigates these changes within the Pacific Northwest region of the United States, encompassing a mix of urban and rural contexts with diverse socio-demographic characteristics. Using survey data from 807 respondents, the authors analyze transportation patterns before and during the pandemic, focusing on shifts in mode shares and probabilities of switching travel modes. The analysis incorporates McNemar’s test, logistic regression, and latent class analysis (LCA) to evaluate the extent of these shifts and identify key influencing factors. The results reveal a substantial reduction in public transport usage, reflecting heightened concerns over health risks and limited operational capacity during the pandemic. In contrast, there was a notable increase in the use of private vehicles and active transportation modes, such as walking and cycling. Demographic variables, including age, income, employment status, and gender, played significant roles in shaping travel behavior, with younger and lower-income individuals exhibiting higher probabilities of mode change. The latent class analysis highlighted distinct behavioral clusters, indicating that travel behavior responses were not uniform across populations. A logistic regression model further underscored the importance of pre-pandemic travel habits, socio-economic conditions, and pandemic-related concerns in influencing mode choice decisions. Additionally, traffic safety outcomes showed notable variations, with overall crash rates decreasing during the lockdowns but fatality rates rising due to riskier driving behaviors, such as speeding on roads. Crash patterns varied across urban and rural areas, with urban crashes experiencing a slight decline in proportion, while rural crashes increased.
The global pandemic, which started around early 2020, significantly disrupted life for many families, and the trip to and from school was not immune to these disruptions. Parents and children alike made travel adjustments depending on their preferences with regard to personal health and safety, social distancing, and aversion to risk. Each school district and individual school also made decisions with regard to in-person or remote learning during this period of uncertainty. In this study, the research team examines how the pandemic affected school transportation for hundreds of families across the Pacific Northwest. An online survey was developed and administered with the help of Qualtrics, an experience management company. Over 600 responses were gathered to assess school transportation-related travel decisions. In addition to collecting demographic data about the respondents, the survey also asked about travel mode choices and characteristics of the trip to and from school. The collective results were then analyzed to determine which factors directly contributed to pandemic-related changes in travel behavior. The study concluded that the demographic factors of parent education level, household income, and age of child were all statistically significant variables that affected behavioral change, though the place of household residence, whether rural or urban, was determined to be an insignificant variable. Additionally, common travel assumptions associated with rural students, when compared with urban students, were confirmed. These factors included a greater reliance on a yellow school bus and lesser availability of critical infrastructure.
People’s attitudinal shifts toward an epidemic at different stages of the epidemic affect their travel behavior. Non-commuting travel behavior is more variable than commuting, as non-commuters have more travel options. However, few studies explored the changes in non-commuting travel and its influencing factors across different stages of sudden and localized COVID-19 outbreaks. Using survey data collected in Nanjing, China, where there was a sudden and localized outbreak of COVID-19, this research adopted the random parameter ordered logit model with heterogeneity in means and variances (HMV) to explore the factors influencing non-commuting travel in the early, middle, and late epidemic stages. The model results revealed that considering the HMV would improve the model fitness. In addition, the temporal stability of factors was investigated via a likelihood ratio test, which confirms traveler behavioral differences across different stages. The results showed that “e-bike ownership” and “the number of PCR (polymerase chain reaction) tests” is positively correlated with the number of non-commuting trips over three epidemic stages. The variables “people who live together have a red health code,”“mask replacement frequency,” and “risk-free areas” are significant in the early-stage and middle-stage models. The variables “people who live together have a green health code all the time” only become significant in the late-stage model. Research findings contribute to the understanding of non-commuting behaviors and targeted management needs during local outbreaks, and can help the government address travel issues under future major health events.
The Covid-19 pandemic significantly impacted transit ridership across Canada. As the pandemic begins to subside, understanding the factors that influence peoples’ decisions to use transit (or not) is crucial for the recovery and long-term sustainability of public transit. Using data from the third wave of the Public Transit and Covid-19 survey in Canada, this study evaluates who returned to pre-pandemic transit use, the factors influencing the decision to ride transit, and peoples’ intentions for future transit use. The authors find that most transit riders perceive that the pandemic is over but its effects are here to stay, though they are split about whether the pandemic still affects their transit use. While some transit riders have gradually returned to pre-pandemic transit levels, a relatively small share of those who have not yet fully returned intend to and a significant proportion do not intend to fully return. About half of transit riders will return to transit at a lower usage level than before the pandemic, while about 10% do not intend to return at all. The results indicate that in the “new normal”, transit use will remain below pre-pandemic levels for those who rode transit before the pandemic. Factors such as car access are significantly related to the extent to which people have returned to transit, although this may be reflecting a shift away from transit rather than causing the shift. Factors such as easy access to transit stops, service frequency, and proximity to home and job locations influence current transit use.
The purpose of this research was to determine the relationship between traffic movements and COVID-19 infections, and ultimately hospitalizations and deaths, throughout various U.S. States using the infection curve and equations from the Susceptible-Infected Recovered (SIR) model. As a result of state and national governmental restrictions and public perception of the virus, traffic patterns were severely altered throughout the peak of the pandemic in 2020 and 2021. Traffic volumes experienced the greatest reduction when governmental restrictions were first enforced at the beginning of the pandemic and began to approach pre-pandemic values during 2021 as facilities throughout the country reopened. The prediction model applies the traffic volume conditions during the initial stage of the pandemic to the entire study period to determine the effect traffic volumes have on COVID-19 infections. Once the observed infection data were modeled, the adjusted, predicted model was determined using a series of modified SIR equations that reflect changes in traffic, and the findings suggest infection numbers may have been reduced compared to the observed data for each U.S. state studied. The number of hospitalizations and deaths that may be reduced during the second peak given the traffic conditions from the beginning of the pandemic were calculated based on the predicted model results for each state. The findings suggested by the predicted model (i.e., a reduction in infections, hospitalizations, and deaths) can benefit health service facilities by limiting overcrowding and the shortage of ventilators, which can result in fewer deaths caused by COVID-19. This research provides insights for practitioners, researchers, and government entities developing and accessing plans for future pandemics. It is also expected that the findings of this study can be built upon by future researchers who continue to study various aspects of the COVID-19 pandemic and assess the public response to governmental actions.
Safety concerns, social distancing requirements, and limited on-road vehicles during the pandemic have resulted in disruptions or reduced operations in many public transportation services, exposing underlying transportation mobility problems. Despite these challenges and travel restrictions, essential travel demands persist, such as accessing healthcare, grocery stores, employment opportunities, and other fundamental services. However, due to the decreased service frequency of buses, trains, and other modes of public transit, vulnerable groups are disproportionately affected by limited mobility options compared to other populations. This exacerbates existing inequalities and affects their ability to meet essential needs under this challenging circumstance. Disadvantaged and disabled travelers often rely more on public transportation to access those essential services for its affordable ticket prices. Therefore, addressing these social exclusion challenges and providing a minimal transportation service level is crucial to ensure equitable access to essential services for vulnerable populations throughout the pandemic and beyond. To improve the life quality of people with disabilities and elderly people by addressing social exclusion, accessibility, and mobility issues, previous team work has developed a car-sharing optimization model with Sample Average Approximation (SAA) and Rolling Horizon (RH) methods, so that city planners, Non-profit Organizations (NPOs), and some other policymakers are enabled to better allocate limited resources to implement the car-sharing system when little to no historical travel information is available for low-density population areas. Nevertheless, understanding and implementing the technical algorithm and framework can be challenging for individuals who lack sufficient training, primarily due to its complexity and the absence of user-friendly interfaces and/or step-by-step user manual guidance. In order to reduce the application barrier for general users, this project aims to develop a more user-friendly Graphical User Interface (GUI) using the Tkinter library within Python and provide comprehensive manual guidance.
