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.

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.

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.