This thesis contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study- a) Linear regression model, b) Spatial Autoregressive Model (SAR) and c) Spatial Error Model (SEM). For the model estimation, traffic volume data for the year 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 good fit and intuitive parameter estimates. The significant value of spatial autocorrelation coefficients in the SEM models support 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 the holidays, was found to have declined after March 2020. In addition, change in daily COVID-19 cases was found to have larger impact on South Florida (District 6)'s travel demand on weekdays compared to other parts of the state. Further, the gradual increase of traffic demand due to the rapid vaccination was also demonstrated in this study. The model system will help transportation researchers and policy makers understand the changes in traffic volume during the COVID-19 period as well as it's spatiotemporal recovery.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2224 |
| Date | 01 January 2022 |
| Creators | Jahan, Md Istiak |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
Page generated in 0.002 seconds