Identifying the latent relationships between factors associated with traffic crashes through graphical models.

Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on the problem at hand. Despite the importance and success of this approach, there are two issues that are generally not discussed: 1) the latent relationships between factors associated with crashes are oftentimes not the focus of analysis or not observed; and 2) there are not many tools to make informed decisions on which variables might have an impact on the crash outcome and should be included in a safety model, particularly when observations are limited. To address these issues, this paper proposes the use of graphical models, namely a Markov random field (MRF) modelling, Bayesian network modelling, and a graphical XGBoost approach, to disclose relationship topologies of explanatory variables leading to fatal and incapacitating injury pedestrian crashes. The application of graph learning models in traffic safety has a high potential because they are not only useful to understand the mechanism behind the crash occurrence but also can assist in devising accurate and reliable prevention measures by identifying the true variable structure and essential factors jointly acting towards crash occurrence, similar to a pathological examination.

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida.

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Traffic monitoring system design considering multi-hazard disaster risks.

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

Older Adults' concerns regarding Hurricane-Induced evacuations during COVID-19: Questionnaire findings.

The COVID-19 pandemic has drastically affected our day-to-day life in the last few years. This problem becomes even more challenging when older adults are considered due to their less powerful immune system and vulnerability to infectious diseases, especially in Florida where 4.5 million people aged 65 and over reside. With its long coastline, large and rapidly growing of older adult population, and geographic diversity, Florida is also uniquely vulnerable to hurricanes, which significantly increases the associated risks of COVID-19 even further. This study investigates older adults' evacuation-related concerns during COVID-19 using statistical analysis of a questionnaire conducted among 389 older adult Florida residents. The questionnaire includes questions concerning demographic information and older adults' attitudes toward hurricane-induced evacuations during the COVID-19 pandemic. Ordered Probit regression models were developed to investigate the impacts of demographic parameters on older adults' tendencies toward evacuating as well as their preferences to stay at home or shelter during the pandemic. The model results reveal that male participants felt safer to evacuate compared to females. Also, any decrease in the level of income was associated with an increase in the need for help for evacuation by 18%. Findings indicated that the participants who found the evacuation safe normally also had a positive attitude toward staying in their vehicle, hotel, or even shelters if maintaining social distance was possible. Emergency management policies can utilize these findings to enhance hurricane preparations for dealing with the additional health risks posed by the pandemic for older adults, a situation that could be exacerbated by the upcoming hurricane season in Florida.

Identifying areas of potential critical healthcare shortages: A case study of spatial accessibility to ICU beds during the COVID-19 pandemic in Florida.

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.

Spatial accessibility assessment of COVID-19 patients to healthcare facilities: A case study of Florida.

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.

Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data.

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.

Rainfall Runoff and Flood Simulations for Hurricane Impacts on Woonasquatucket River, USA.

Integrated hydrological and hydrodynamic modeling study has been conducted to investigate hurricane impact on Woonasquatucket River, Rhode Island, USA. Model simulation was conducted for the case study of 2010 storm event. The hydrological model simulates the runoff from the heavy rainstorm, while the river hydrodynamic model simulates the flood waves affected by the interactions of upstream rainfall runoff and downstream storm surge. Results indicate that the river floods was dominant by rainfall runoff in upper river reaches, but dominant by storm surge in the lower river area near the estuary.

Evacuating People and Their Pets: Older Floridians' Need for and Proximity to Pet-Friendly Shelters.

OBJECTIVES: Pets influence evacuation decisions, but little is known about pet-friendly emergency shelters' availability or older adults' need for them. Our study addresses this issue, focusing on the most densely populated area of Florida (Miami-Dade)-the state with the oldest population and greatest hurricane susceptibility. METHOD: We use Geographic Information Systems (GIS)-based methodology to identify the shortest paths to pet-friendly shelters, based on distance and congested and uncongested travel times-taking into account the older population's spatial distribution. Logistic regression models using the 2013 American Housing Survey's Disaster Planning Module examine anticipated shelter use as a function of pet ownership and requiring pet evacuation assistance. RESULTS: Thirty-four percent of older adults in the Miami-Dade area have pets-35% of whom report needing pet evacuation assistance. However, GIS accessibility measures show that travel time factors are likely to impede older adults' use of the area's few pet-friendly shelters. Logistic regression results reveal that pet owners are less likely to report anticipating shelter use; however, the opposite holds for pet owners reporting they would need help evacuating their pets-they anticipate using shelters. DISCUSSION: High pet shelter need coupled with low availability exacerbates older adults' heightened vulnerability during Florida's hurricane season.

Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations.

The increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users.

Special needs hurricane shelters and the ageing population: development of a methodology and a case study application.

Recent experience of hurricanes, particularly in the southeast United States, has heightened awareness of the multifaceted nature of and the challenges to effective disaster relief planning. One key element of this planning is providing adequate shelter at secure locations for people who evacuate. Some of these individuals will have 'special needs', yet there is little research on the relationship with shelter space. This study designed a geographic information systems-based network optimisation methodology for the siting of special needs hurricane relief shelters, with a focus on the transportation component. It sought to find new locations for shelters that maximise accessibility by vulnerable populations, given capacity constraints, concentrating on the ageing population. The framework was implemented in a medium-sized metropolitan statistical area in the state of Florida where data suggest a possible deficit in special needs shelter space. The study analysed options for increasing special needs shelter capacity, while considering potential uncertainties in transportation network availability.

Emergency inventory management for disasters--a review.

There has been a recent surge in the publication of academic literature examining various aspects of emergency inventory management for disasters. This article contains a timely literature review of these studies, beginning with an exposition of the characteristics of storage and delivery options for emergency supplies, with a particular emphasis on the differences between emergency inventories and conventional inventory management. Using a novel classification scheme and a comprehensive search of the inventory related literature, an overview of the emergency inventory management studies is also presented. Finally, based on this extensive review, a discussion is presented based on the critical issues and key findings related to the emergency inventory management field, and include suggestions for future research directions.