The coronavirus disease of 2019 (COVID-19) pandemic has affected every aspect of peoples' lives, including their mobility. In this study, the impact of closures related to the pandemic on traffic patterns is assessed for the state of North Carolina and the Commonwealth of Virginia. The results of the initial assessments indicated that the average reduction in traffic volume was 27% across North Carolina and Virginia. Findings suggest that a slight increase in traffic at the beginning of the stay-at-home orders was followed by at most a 40% reduction after closures. The changes in traffic volumes in terms of road and vehicle classification are most substantial in Interstate routes and for passenger cars and buses. The average recovery rate of traffic volume after the occurrence of the maximum reductions was between 2.3% and 3.4% per week.
The objective of this study was to develop an approach for incorporating techniques used to interpret and evaluate deflection data for network-level pavement management system applications. A national pavement management system is being developed in Iran and the use of falling weight deflectometers (FWDs) at the network level was deemed necessary to compensate for the lack of vital construction history data in the pavement inventory. Because FWD measurements disrupt traffic flow and are a potential safety hazard, it is imperative to increase the interval between FWD testing points as much as possible to allow scanning of the entire 51,000 km network of freeways, highways, and major roads in a reasonable time span with the least traffic disruption. A project-level dataset at 0.2 km intervals in different environments and diverse traffic categories was selected for analysis. In addition, data from continuous ground-penetrating radar was collected concurrently and compared with a limited number of cores. The overall analysis included evaluation of interval variation, segmentation, the structural condition index (SCI), and layer moduli calculated using the AASHTO and ELMOD methods. The analysis was done to determine the optimum interval between test points. Analysis showed that the collection intervals could be increased from 0.2 to 0.6 km. Subsequently, the applicability and time efficiency of the network-level intervals were verified by calculating overlay thickness and time required.
The recent increase in the frequency of extreme weather events has raised awareness and interest in the need for transportation infrastructure resilience. In this paper, the issue of pavement resilience is discussed with the goal of refining the idea for its use in pavement design. It is argued that one critical knowledge gap to advancing the state of the art in this area is distinguishing between functional and structural resiliency. The arguments here are framed using floods and heatwaves to demonstrate the importance of structural resilience. Under extreme event disruptions, structural damage is inevitable. The case study simulations in this paper suggest, depending on the pavement structure, intensity, and frequency of flooding events over the analysis period, that pavement rutting performance can decrease by 15.5% in the case of a structure with sand subgrade and 18.8% in the case of a structure with clay subgrade. In the case of heatwaves, the increase in rutting was found to be 2.9% in a structure with sand subgrade. To move toward more resilient pavement infrastructure, it is important to continuously monitor pavements after extreme events, develop methodologies to predict their performance, incorporate the findings in the current pavement management systems, and adapt design and management strategies accordingly. Future design and management of pavement systems should consider both structural and functional resiliency. This study shows that pavement performance simulations predict a long-term decrease in structural performance as a result of extreme events.
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