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Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially those caused by extreme events, quickly affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult because of the stochastic nature of such events. Nevertheless, it is possible to know in advance which areas are more vulnerable and plan accordingly. This paper aims to use remote sensing techniques based on satellite images to detect roadways’ vulnerabilities to hurricanes. Each road is assigned a vulnerability score based on the information retrieved from the satellite image and other geographic information system (GIS) data, which can be used to create a vulnerability map of the area. Both vegetation exposure along the roads and the consequences of the closures are considered in the vulnerability assessment. The study area has been selected in Tallahassee, Florida, U.S., where a high-resolution satellite image was acquired in September 2018, 2 weeks before Hurricane Michael. The findings of this work can help management teams and city responders to identify the most vulnerable regions at risk of disruption and organize resources before the event.

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

This study explored the use of real-time traffic events and signal timing data to determine the factors influencing the injury severity of vehicle occupants at intersections. The analysis was based on 3 years (2017–2019) of crash and high-resolution traffic data. The best fit regression was first identified by comparing the conventional regression model and logistic models with random effect. The logistic model with a heavy-tailed distribution random effect best fitted the data set, and it was used in the variable assessment. The model results revealed that about 13.6% of the unobserved heterogeneity comes from site-specific variations, which underlines the need to use the logistic model with a random effect. Among the real-time traffic events and signal-based variables, approach delay and platoon ratio significantly influenced the injury severity of vehicle occupants at 90% Bayesian credible interval. Additionally, the manner of a collision, occupant seat position, number of vehicles involved in a crash, gender, age, lighting condition, and day of the week significantly affected the vehicle occupant injury. The study findings are anticipated to provide valuable insights to transportation agencies for developing countermeasures to mitigate the crash severity risk proactively.

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.

Healthcare resource availability is potentially associated with COVID-19 mortality, and the potentially uneven geographical distribution of resources is a looming concern in the global pandemic. Given that access to healthcare resources is important to overall population health, assessing COVID-19 patients' access to healthcare resources is needed. This paper aims to examine the temporal variations in the spatial accessibility of the U.S. COVID-19 patients to medical facilities, identify areas that are likely to be overwhelmed by the COVID-19 pandemic, and explore associations of low access areas with their socioeconomic and demographic characteristics. We use a three-step floating catchment area method, spatial statistics, and logistic regression to achieve the goals. Findings of this research in the State of Florida revealed that North Florida, rural areas, and zip codes with more Latino or Hispanic populations are more likely to have lower access than other regions during the COVID-19 pandemic. Our approach can help policymakers identify potentially possible low access areas and establish appropriate policy intervention paying attention to those areas during a pandemic.

Hurricanes affect millions of people in the U.S. every year and cause billion-dollar economic losses. Florida is one of the states in the U.S. which is vulnerable to hurricanes and major infrastruc...

The State of Florida is significantly vulnerable to catastrophic hurricanes that cause widespread infrastructural damage and claim lives annually. In 2017, Hurricane Irma, a Category 4 hurricane, took on the entirety of Florida, causing the state’s largest evacuation ever as 7 million residents fled the hurricane. Floridians fleeing the hurricane faced the unique challenge of where to go, since Irma made an unusual landfall from the south, enveloping the entire state, forcing evacuees to drive farther north, and creating traffic jams along Florida’s evacuation routes that were worse than during any other hurricane in Florida's history. This study aimed to assess the spatiotemporal traffic impacts of Irma on Florida’s major highways based on real-time traffic data before, during, and after the hurricane made landfall. First, we conducted a time-series-based analysis to evaluate the temporal evacuation patterns of this large-scale evacuation. Second, we developed a metric, namely the congestion index (CI), to assess the spatiotemporal evacuation patterns on I-95, I-75, I-10, I-4, and turnpike (SR-91) highways with a focus on both evacuation and returning traffic. Third, we employed a geographic information system-based analysis to visually illustrate the CI values of corresponding highway sections with respect to different dates and times. Findings clearly showed that imperfect forecasts and the uncertainty surrounding Irma’s predicted path resulted in high levels of congestion and severe delays on Florida’s major evacuation routes.

The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.

Public libraries are critical community facilities through which librarians support their communities before, during, and after natural disasters such as hurricanes. In their disaster responses, pu...

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018

During the COVID-19 pandemic, healthcare facilities worldwide have been overwhelmed by the amount of coronavirus patients needed to be served. Similarly, the U.S. also experienced a shortage of healthcare resources, which led to a reduction in the efficiency of the whole healthcare system. In order to evaluate this from a transportation perspective, it is critical to understand the extent to which healthcare facilities with intensive care unit (ICU) beds are available in both urban and rural areas. As such, this study aims to assess the spatial accessibility of COVID-19 patients to healthcare facilities in the State of Florida. For this purpose, two methods were used: the two-step floating catchment area (2SFCA) and the enhanced two-step floating catchment area (E2SFCA). These methods were applied to identify the high and low access areas in the entire state. Furthermore, a metric, namely the Accessibility Ratio Difference (ARD), was developed to evaluate the spatial access difference between the models. Results revealed that many areas in the northwest and southern Florida have lower access compared to other locations. The residents in central Florida (e.g., Tampa and Orlando cities) had the highest level of accessibility given their higher access ratios. We also observed that the 2SFCA method overestimates the accessibility in the areas with a lower number of ICU beds due to the "equal access" assumption of the population within the catchment area. The findings of this study can provide valuable insights and information for state officials and decision makers in the field of public health.

Intersections are among the most dangerous roadway facilities due to the existence of complex movements of traffic. Most of the previous intersection safety studies are conducted based on static and highly aggregated data such as average daily traffic and crash frequency. The aggregated data may result in unreliable findings because they are based on averages and might not necessarily represent the actual conditions at the time of the crash. This study uses real-time event-based detection records, and crash data to develop predictive models for the vehicle occupants' injury severity. The three-year (2017-2019) data were acquired from the arterial highways in the City of Tallahassee, Florida. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were used to identify the important factors on the vehicle occupants' injury severity prediction. The performance comparison of the two classifiers revealed that the XGBoost has a higher balanced accuracy score than RF. Using the XGBoost classifier, five topmost influential factors on injury prediction were identified. The factors are the manner of the collision, through and right-turn traffic volume, arrival on red for through and right-turn traffic, split failure for through traffic, and delays for through and right-turn traffic. Moreover, the partial dependency plots of the influential variables are presented to reveal their impact on vehicle occupant injury prediction. The knowledge gained from this study will be useful in developing effective proactive countermeasures to mitigate intersection-related crash injuries in real-time.

Conventional intersection designs are known to be problematic and unreliable when handling the complexity associated with the heavy traffic volume and travel demand on today’s roadways. Alternative innovative and safer designs such as the restricted crossing U-turn (RCUT) intersection can address these complex problems. However, there is still a gap in the literature concerning the safety performance analysis of RCUT intersections. Consistent with this goal, a comprehensive search was performed to identify RCUTs in the US and collect the relevant data needed to create appropriate safety performance functions and crash modification factors/functions for RCUTs. As such, different safety performance function (SPF) and crash modification factors/function (CMF) models were developed for signalized and unsignalized RCUTs with a focus on all crashes or fatal and injury crashes only, which span from complex models to relatively simple and easily implementable models. This can aid in creating a flexibility for safety agencies or officials that can prefer more complex models when sufficient data are available. Findings indicate that RCUTs have the potential to reduce the number of fatal and injury (F&I) crashes substantially at problematic locations and specifically illustrate that the selection of RCUT location depends significantly on the traffic volumes of major and minor approaches as well as their ratios. The developed SPFs and CMFs can be successfully used by transportation agencies to make informed decisions on the evaluation and justification of the installation of RCUTs.

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.

The demand for mental health services has been growing stronger over the last couple of decades. This indicates the need to study and assess the access to these mental health services especially with a focus on the vulnerable populations having the greatest need. As such, this paper presents a Geographical Information Systems (GIS)-based analysis in order to study and evaluate the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility metrics are calculated with regard to healthcare facilities using travel times between population block groups and these critical mental health facilities. These estimates are used to calculate the weighted county accessibility scores for each county. Findings clearly delineate those counties that lack access to mental facilities, especially those in Northwest Florida, a demographically diverse and substantially rural region. This type of analysis can help planners and policy makers develop better strategies in order to provide adequate mental health care options needed in targeted locations.

Shelters serve as critical facilities where people gather during and after hurricanes. A basic requirement of ‘operation’ for a shelter is being functional and available to help. However, what happens when shelters themselves are damaged and unable to serve those people? An open question with key policy implications is: How can we identify the most critical shelter(s) as a part of broader emergency evacuation operations and how can we respond to an interdiction? This paper develops a two-step modeling framework consisting of enhanced r-interdiction median models (RIM) to identify the most significant shelter(s) and revised p-median models to identify shelters to repurpose during such an interdiction where shelters are rendered off-line. Proposed models are applied on a Southeast Florida case study with respect to scenarios based on varying hurricane strength and shelter demand. Findings indicate that models are susceptible to travel cost variation based on the demand-weighted objectives, and that shelter selections vary due to different demand scenarios created which focus on different population segments. As hurricane strength increases, critical shelter identification is driven by flooding and storm surge risks. These findings can inform efforts to harden those critical shelters so that they can better serve populations in need.