Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and statistical analyses on power outages and roadway closures. First, a geographical information systems (GIS)-based spatial analysis was conducted to explore the power outages and roadway closure patterns in the city including kernel density estimation (KDE) and density ratio difference (DRD) methods. In order to provide a more detailed assessment on which population segments were more affected, a second step included a statistical analysis to identify the relationships between demographic- and socioeconomic-related variables and the magnitude of power outages and roadway closures caused by these hurricanes. The results indicate that the high-risk locations for roadway closures showed different patterns, whereas power outages seemed to have similar spatial patterns for the hurricanes. The findings of this study can provide useful insights and information for city officials to identify the most vulnerable regions which are under the risk of disruption. This can lead to better infrastructure plans and policies.
This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas.
Roadway crashes are an increasing concern worldwide, and pedestrians are among the most vulnerable road users because of several special characteristics, including their fragility. While focusing on pedestrian-involved crashes that occurred far away from intersections, a more systematic investigation is needed to determine whether there is a relationship between the severity of these crashes and the land use types surrounding the crash locations. To investigate this issue, a network-based logistic regression model was employed to assess the impact of the distance between pedestrian-involved crash locations and the centroid of an associated land use of various types. In addition, a set of logistic models was developed to assess the effect of traffic- and environment-related factors, such as daylight and the average annual daily traffic (AADT), on the severity of crashes occurring around each land use. Several types of land use appeared to be influential in predicting pedestrian-involved crash severity. The probability of a severe pedestrian-involved crash increased around commercial land use types including retail stores or night clubs whereas it decreased around university campuses. Moreover, among the crash attributes, daylight contributed to the severity of pedestrian-involved crashes regardless of the surrounding land use type while average traffic was found to be a statistically significant factor for the crashes that occurred particularly around parking lots and office buildings. Pedestrian-involved crashes mostly occurred in the vicinity of office buildings during daylight hours, and the pedestrians were less likely to be severely injured. The findings provided valuable insight into the measurements concerning pedestrian safety with respect to various types of land use.
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