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Air mobility has been a military strategic advantage used by the United States (U.S.) from the onset of aircraft carriers, to supporting air bases worldwide. The U.S. government and defense components rely heavily on a civilian fleet of aircraft to supplement air transportation requirements in both peace times and during national emergencies. This paper reviews the historical and legal development of the Civil Reserve Air Fleet (CRAF) and discusses previous struggles and successes of the program by looking at the functionality of the program, before addressing how current events bring about the realization that the program must change. Current changes in the way U.S. airlines operate, the way warfare has been changed, and the financial hardships associated with the COVID-19 pandemic are all used to envision a future of the CRAF program to provide future air transportation capabilities to allow the U.S. government to maintain the necessary strategic advantage of responsive airlift capabilities.

This study analyzes the impact of the COVID-19 pandemic on traffic congestion in 15 metropolitan areas of 13 Latin American countries. The database of the Traffic Congestion Intensity (TCI) of the IDB Invest Dashboard is used, it was developed from the alliance between the IDB and Waze and it is correlated with the contagions of the population published by Johns Hopkins Hospital University, for the period from March 9 to July 31, 2020, approximately five (5) months. For the analysis, the areas have been categorized into four (4) clusters, based on the Coefficient of Variation and the TCI/ WHO ratio. For each cluster, the graphs of the variation of the ΔTCI, the contagion cases, and the mobility recovery rate are analyzed. Among the conclusions include that the decrease in the number of infections and the flexibility of social distancing measures can be related to a recovery from congestion and that this can be measured as a function to the rate of recovery of mobility. In addition, the pandemic has revealed less collective and more agile forms of mobility, being this an important opportunity for the region to develop new forms of transport.

Rapid transit systems are critical components of urban public transportation networks in their impact, not only on personal mobility but also on the energy and environmental costs associated with network operations. To facilitate effective planning for current and future needs, a framework is required that provides important consumption metrics and also explains the various contributors to energy consumption, along with their interactions. To address this gap, we estimated models that utilized operational and ridership data for the Massachusetts Bay Transportation Authority’s rapid transit system, as well as ambient temperature, to accurately predict system-wide electricity consumption. The models were trained with data from 2019 and tested with data from 2020. The estimated multiple linear regression (MLR) and random forest (RF) models explained 93% and 95% of the variance in the data set, respectively. The MLR model provided predictions with a root mean squared error (RMSE) of 2.7 MWh and mean absolute percentage error (MAPE) of 4.68%, while the RF model resulted in an RMSE of 2.94 MWh and MAPE of 5.01%. We also investigated the impacts of COVID-19 on the transit system by exploring the effects on ridership, energy consumption, cost, and train movement metrics before and during the pandemic. We find that the models are robust and perform well, even with the significant disruptions associated with the COVID-19 pandemic.

In most cases, the decision to invest in a new airport is not simple, mainly because of the complications in the planning process, the amount of capital that needs to be invested before the establishment of the business, and the number of stakeholders involved in the decision. The decision process is more complicated in restricted economic and financing conditions, where the performance of the business plan is strongly related to regional development prospects and future airport business outputs in the medium and long term. This paper provides an evaluation methodology approach to support decisions on airport development projects. The proposed methodology provides an evaluation framework based on a combination of an ex ante assessment analysis, considering the airport’s economic impact and its contribution to a specific regional economy. The Input–Output (IO) analysis framework is used to determine the economic footprint of the airport development. A series of key performance indicators (KPIs) are introduced to review the project performance in a given economic system. The case study is examined, focussing on a new airport at Heraklion in Crete (in the Kasteli valley), one of the most attractive tourist destinations in the southeast Mediterranean. Conventional wisdom is to present a systematic approach appropriate to relevant projects, providing essential tools that support decisions at the level of strategic planning. The approach is essential to provide key messages to national governments, decision makers, and stakeholders on the contribution of an airport investment to regional economic development and its contribution to the business ecosystem in the post-COVID-19 era.

The New York City metropolitan area was hard hit by COVID-19, and the pandemic brought with it unprecedented challenges for New York City Transit. This paper addresses the techniques used to estimate dramatically changing ridership, at a time when previously dependable sources suddenly became unavailable (e.g., local bus payment data, manual field checks). The paper describes alterations to ridership models, as well as the expanding use of automated passenger counters, including validation of new technology and scaling to account for partial data availability. The paper then examines the trends in subway and bus ridership. Peak periods shifted by both time of day and relative intensity compared with the rest of the day, but not in the same way on weekdays and weekends. On average, trip distances became longer for subway and local bus routes, but overall average bus trip distances decreased owing to a drop in express bus usage. Subway ridership changes were compared with neighborhood demographic statistics and numerous correlations were identified, including with employment, income, and race and ethnicity. Other factors, such as the presence of hospitals, were not found to be significant.

Transit ridership has been seriously affected around the world by the COVID-19 pandemic. This study investigates the impacts of the COVID-19 pandemic on bus service ridership patterns in King County, Washington, using clustering and multinomial logit (MNL) models. Ridership patterns of King County Metro buses during different study periods are detected using clustering. The characteristics of ridership patterns and cluster assignment spatial distributions are further examined. The MNL models were developed using explanatory factors, including socio-demographic, transit service, and land use characteristics at each stop, that are correlated with the ridership pattern cluster assignments. Results of the developed models demonstrate disparities across socio-economic groups and unevenness throughout different neighborhoods in ridership reduction and peaking patterns during COVID-19.

The present research addresses the influence of COVID-19 on the amount of air pollutants induced by commercial air transport in Turkey. The data sets were obtained from the General Directorate of State Airports Authority (GDSA), the International Civil Aviation Organization (ICAO) and Turkish Airlines (THY). Since the pandemic became a serious issue for Turkey in March, 2020, the period from March until August 2020 is considered as being the pandemic period for the purpose of calculations. Comparisons were added to computations starting from January 2017. The percentage changes in the total pollutant amount on a monthly basis, induced by domestic flights in 2020 for the months of March, April, May, June, July and August were calculated to be -42.78%, -99.76%, -99.61, -73.27, -49.66% and -36.66%, respectively. Compared to the total amount of emissions of domestic traffic, the total amount of pollutants for international traffic did not increase in July and August. The increase in traffic on domestic flights in July and August 2020, along with the increase in demand for the tourism sector, did not reflect the same on international routes. The fact that part of the data obtained during the study is based on real evidence, and that some of it gives an average result through assumptions, indicates a serious decrease in the amount of pollutants as an undeniable fact.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

In the absence of a vaccine, nonpharmaceutical interventions such as social distancing and travel reductions were the only strategies for slowing the spread of the COVID-19 pandemic. Using survey data from Hawaii ( n = 22,200) collected in March through May of 2020 at the onset of the pandemic, the differences between traveler spreaders who brought the disease into the state and community spreaders were investigated. In addition to describing the demographic attributes and comparing them with attributes of those who were vulnerable to COVID-19, logit models explaining travel behaviors were developed and tested. Traveler spreaders were likely to be male, younger, and returning students. Community spreaders were more likely to be male, essential workers, first responders, and medical personnel at the highest risk of exposure. Using spatial statistics, clusters and hotspot locations of high-risk individuals were mapped. As transportation researchers are in a position to combine their critical analytical capabilities and experience with relevant databases on mobility and the spread of infectious diseases, this analysis could support efforts to respond to and slow the spread of the pandemic.

This work investigated the impacts of COVID-19 on pedestrian behavior, answering two research questions using pedestrian push-button data from Utah traffic signals: How did push-button utilization change during the early pandemic, owing to concerns over disease spread through high-touch surfaces? How did the accuracy of pedestrian volume estimation models (developed pre-COVID based on push-button traffic signal data) change during the early pandemic? To answer these questions, we first recorded videos, counted pedestrians, and collected push-button data from traffic signal controllers at 11 intersections in Utah in 2019 and 2020. We then compared changes in push-button presses per pedestrian (to measure utilization), as well as model prediction errors (to measure accuracy), between the two years. Our first hypothesis of decreased push-button utilization was partially supported. The changes in utilization at most (seven) signals were not statistically significant; yet, the aggregate results (using 10 of 11 signals) saw a decrease from 2.1 to 1.5 presses per person. Our second hypothesis of no degradation of model accuracy was supported. There was no statistically significant change in accuracy when aggregating across nine signals, and the models were actually more accurate in 2020 for the other two signals. Overall, we concluded that COVID-19 did not significantly deter people from using push-buttons at most signals in Utah, and that the pedestrian volume estimation methods developed in 2019 probably do not need to be recalibrated to work for COVID conditions. This information may be useful for public health actions, signal operations, and pedestrian planning.