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.

The recent COVID-19 pandemic has prompted global governments to take several measures to limit and contain the spread of the novel virus. In the United States (US), most states have imposed a partial to complete lockdown that has led to decreased traffic volumes and reduced vehicle emissions. In this study, we investigate the impacts of the pandemic-related lockdown on air quality in the US using remote sensing products for nitrogen dioxide tropospheric column (NO2), carbon monoxide atmospheric column (CO), tropospheric ozone column (O3), and aerosol optical depth (AOD). We focus on states with distinctive anomalies and high traffic volume, New York (NY), Illinois (IL), Florida (FL), Texas (TX), and California (CA). We evaluate the effectiveness of reduced traffic volume to improve air quality by comparing the significant reductions during the pandemic to the interannual variability (IAV) of a respective reference period for each pollutant. We also investigate and address the potential factors that might have contributed to changes in air quality during the pandemic. As a result of the lockdown and the significant reduction in traffic volume, there have been reductions in CO and NO2. These reductions were, in many instances, compensated by local emissions and, or affected by meteorological conditions. Ozone was reduced by varying magnitude in all cases related to the decrease or increase of NO2 concentrations, depending on ozone photochemical sensitivity. Regarding the policy impacts of this large-scale experiment, our results indicate that reduction of traffic volume during the pandemic was effective in improving air quality in regions where traffic is the main pollution source, such as in New York City and FL, while was not effective in reducing pollution events where other pollution sources dominate, such as in IL, TX and CA. Therefore, policies to reduce other emissions sources (e.g., industrial emissions) should also be considered, especially in places where the reduction in traffic volume was not effective in improving air quality (AQ).

Research into the operation of traffic flow at high volumes reveals that the capacity of freeways is not a fixed number but is rather a random variable. Since traditional operational performance measures for the analysis of traffic flow on freeways typically disregard the randomness of capacity, new approaches to make use of the concept of randomness for freeway operation analysis are required. To address that need, this paper introduces a new indicator of freeway performance based solely on a stochastic approach to capacity computation. With this new indicator, the maximum reliable volume that can be carried by a freeway over prolonged time periods was derived from parameters of capacity distribution functions. The breakdown probability corresponding to the optimum volume can be used to select a single value from the capacity distribution function. To explore the empirical relationship between expected values of capacity and the optimum volumes calculated from this new computational process, flow data from several German freeway sections were analyzed. To illustrate the application potential for a sustained flow index, this paper also discusses the bases for increasing the effectiveness of ramp metering and vehicle routing management techniques.

This research was undertaken to comparatively assess the unprecedented travel and activity conditions related to the onset of coronavirus disease of 2019 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.

This paper describes the approach and the results of an ongoing research effort to assess the resilience of port operations following major disasters and other disruptive events. The work presented in this paper used archival data from the U.S. Coast Guard’s nationwide automatic identification system to quantify the state of resiliency of coastal navigation systems. Illustrating the experimental methodology are case study examples that assess the disruptions that resulted from a collision in March 2014 in the Houston Ship Channel, Texas, and from Superstorm Sandy in 2012 on the greater Port of New York and New Jersey. The methods and results can be adapted and implemented for quantitatively evaluating levels of port activity following disruptive events and for a better understanding of the factors that lead to more resilient maritime systems.