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Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Previous studies have used descriptive and parametric statistical models to identify factors that affect WWD crash severity on limited access facilities. This study adopted a combination of non-parametric data mining techniques aiming to recognize the pattern of contributing factors that affect the WWD crash severity on non-limited access facilities. These non-parametric methods can handle heterogeneity in crash datasets well. In this study, hierarchical clustering was used to divide the crash dataset into homogeneous clusters. A random forests analysis was used to select important variables, and decision trees and decision rules were generated to show the underlying pattern and interactions between different factors that affect WWD crash severity. The analysis was based on 1,475 WWD crashes that occurred on arterial streets from 2012-2016 in Florida. Results show that head-on collisions, weekend days, high-speed facilities, crashes involving vehicles entering from a driveway, dark-not lighted roadways, older drivers, and driver impairment are important factors that play a crucial role in WWD crash severity on non-limited access facilities. [ABSTRACT FROM AUTHOR]

Post-hurricane accessibility to critical facilities is crucial for accelerating the recovery process, which can often be disrupted by hurricane-induced flooding. The State of Florida is especially vulnerable to flooding accompanied by hurricanes, which leads to significant roadway closures and accessibility challenges that require special attention. As such, this research aims to analyze changes in accessibility to critical facilities due to Hurricane Irma-induced flooding. Accordingly, this research aims to compare the average accessibility changes to critical facilities for vulnerable populations, including older people, disabled people, and older people with a disability with the general population. To accomplish these goals, flood mapping was done by analyzing Sentinel-1 images before and after Hurricane Irma (2017) using Google Earth Engine. The accessibility change from block groups to critical facilities was analyzed using a cumulative opportunities approach in GIS, considering flooded areas as the barriers, which were later aggregated to the county level. Finally, employing the population-weighted average accessibility method, a comparison of accessibility change of vulnerable and general populations was conducted. The result shows that 1193.80 square km of areas in 43 counties of Florida, mostly in the southern portions, were affected by flooding after Hurricane Irma. Later, the accessibility changes to critical facilities considering 15-min and 30-min travel time thresholds are presented and visualized. The county-wise change in accessibility of vulnerable and general populations was also computed. The knowledge acquired from this research can improve the development of efficient and equitable post-disaster recovery plans. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]