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 pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface; hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types; therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation, a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user's perception of road condition.Keywords: Random process, power spectral pensity, sinusoidal approximation, shaping filter, pavement profile, roughness.
RESUMENLa rugosidad del pavimento es la principal variable que produce la excitación vertical en los vehículos. Los perfiles de pavimento son los principales responsables de (i) la incomodidad percibida por los usuarios y, (ii) las cargas dinámicas generadas en la interfaz entre el neumático y el pavimento, de ahí que su evaluación constituye un paso esencial en un Sistema de Gestión de Pavimentos. El presente documento evalúa dos técnicas específicas utilizadas para simular perfiles de pavimento; estos son los filtros de forma y la aproximación sinusoidal, ambos basados en la Densidad de Potencia Espectral. La rugosidad del pavimento se evaluó utilizando el Índice Rugosidad Internacional (IRI), el cual es el índice más utilizado para caracterizar rugosidad del perfil longitudinal de una vía. En la presente investigación se definieron parámetros apropiados en el proceso de simulación para obtener perfiles de pavimento con rangos específicos de los valores de IRI utilizando ambas técnicas de simulación. Los resultados sugieren que el uso de la aproximación sinusoidal puede generar perfiles aleatorios con valores de IRI que...
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