Evaluation of Freeway Demand in Florida during the COVID-19 Pandemic from a Spatiotemporal Perspective

Abstract

This study contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to the COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study: (1) the linear regression model, (2) the spatial autoregressive model (SAR), and (3) the spatial error model (SEM). For the model estimation, traffic volume data for 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models offered a good fit and intuitive parameter estimates. The significant value of spatial autocorrelation coefficients in the SEM supports our hypothesis that common unobserved factors affect traffic volumes in neighboring detectors. The model results clearly indicate a disruption in normal traffic demand due to the increased transmission rate of COVID-19. The traffic demand for recreational areas, especially on holidays, was found to have declined after March 2020. In addition, change in daily COVID-19 cases was found to have a larger impact on South Florida (District 6)’s freeway demand on weekdays compared to other parts of the state. Further, the gradual increase of demand due to rapid vaccination was also demonstrated in this study. The model system will help transportation researchers and policy makers understand the changes in freeway volume during the COVID-19 pandemic as well as its spatiotemporal recovery. The model framework developed in our study provides transportation planners with insight on infrastructure usage across freeways in Florida. Within this broad context, the study makes three important contributions. First, the study highlights the impact of a host of variables on traffic demand under normal conditions and the varying impact of these variables due to a shock. The model developed quantitatively identifies the varying spatiotemporal influence of variables on demand evolution in response to a shock. The proposed approach can be applied in other contexts such as a recession to reflect changes in traffic demand over time. Second, using the model for Florida provides an understanding of the locations that exhibit faster recovery rates—such as recreational locations in Central and South Florida. Thus, in the future, transportation planning can accommodate for potentially faster recovery in infrastructure usage in these locations. The finding might also be important for policy making to support various economic sectors to diversify the workforce adequately. Finally, the overall framework will also assist policy makers in assessing infrastructure usage over time under various scenarios to obtain inputs for efficient transportation asset management. An accurate estimation of demand over time while recognizing the freight share (not considered in our work) will allow evaluation of infrastructure deterioration and upkeep.