FACILITATING NO-NOTICE EVACUATION THROUGH OPTIMAL PICK-UP LOCATION SELECTION

Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents' arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. Abstract Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents' arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. 3 The primary purpose of a no-notice evacuation is to save people's lives Evacuation time In a short-notice case, people can choose whether to evacuate or not, and when to evacuate In a no-notice scenario, almost everyone in the impacted area evacuates at the time of the disaster occurrence Origin evacuation place In a "short notice" evacuation case, evacuees often start from their homes together with their families. In a "no-notice" scenario, evacuees start evacuation from wherever they are at that time, (i.e., schools, work places, entertainment place, etc.) most likely by themselves. Family gathering process Families gather together before they start to evacuate. Families gather together during or after the evacuation When a no-notice disaster occurs during the daytime, household members may be scattered throughout the road network. Household dependents in facilities such as schools and daycare centers may wait for their families to pick them up; this family pickup behavior could increase individual evacuation time and cause extra delay for other vehicles in the network. When a large amount of vehicles rush into certain places to pick up dependents within a short period of time, bottlenecks could easily be formed for those locations whose entries/exits cannot accommodate heavy traffic. Facilities' current locations may not be well designed for the emergency case and limited entry/exit by itself could be a bottleneck. Relocating facilities' dependents to appropriate sites would eliminate unnecessary bottlenecks and smooth road traffic. Therefore, this study addresses selecting appropriate pickup locations to facilitate no-notice evacuation. A school is a typical facility having a number of carless dependents who need to be picked up. Most school districts have developed an emergency plan, which in summary, requests three kinds of action, i.e., shelter in place, lockdown, and off-site evacuation. According to the Office of the Superintendent, Arlington High School, Massachusetts 7 (2006), shelter-in-place is used when a danger happens outside the school, such as a chemical spill; lock-down is used when a danger is inside the school and makes evacuation impractical; off-site evacuation is used in an extreme emergency situation, and an evacuation location such as another school, church, Boys & Girls Club, Town Hall or ice skating rink, are prearranged for each individual school. This school's existing emergency plans demonstrate that moving dependents to other sites is a feasible strategy. Moreover, some previous studies have involved this issue; for example, Sinuany-Stern and Stern (1993) studied relocating carless people under an emergency situation, where carless households are assumed to move to a certain point first and are then picked up by organized transportation and transported to the shelter, and the households are assumed to use shortcuts and not interfere with road traffic. This study develops a mathematical model to determine optimal pickup locations for facilities; the objective is to maximize the number of evacuees who can successfully pick up dependents and escape from the dangerous zones afterwards. The model is tested for a given network based on the City of Chicago Heights, Illinois. This report is organized as follows. Chapter 1 describes the background and purpose of this study. Chapter 2 reviews the previous studies on evacuation modeling and the location problem. Chapter 3 formulates the optimization model and explains the methodologies adopted in this study. Chapter 4 describes basic information of the case study in Chicago Heights, such as the network, demand, assumptions and scenarios, and presents the results and the sensitivity analysis. Chapter 5 concludes the study and discusses the future directions. Chapter 2 Literature Review This chapter mainly reviews the previous studies on evacuation modeling. Numerous studies on evacuation planning and modeling were conducted since the 1980s, driven by tragic events such as the Three Mile Island nuclear reactor incident in 1979, September 11 terrorist attacks in 2001, and Hurricanes Katrina and Rita in 2005. Those studies generally focused on estimating evacuation time and determining optimal evacuation routes and optimal shelter locations, using operations research methods and simulation models. Evacuation studies, according to scopes and features of impacted areas, fall into five general categories: regions, neighborhoods, buildings, ships, and airplanes Regional Evacuation Regional (urban) evacuation models can be classified into aggregate models and disaggregate models. An aggregate model investigates a group of vehicles as a whole, while a disaggregate model evaluates each individual driver's behavior. An aggregate model overlooks the difference of individual driver's behavior among the population. The model developed in this study is a disaggregate model that relies on microsimulation. Aggregate Evacuation Modeling Most evacuation models were developed on an aggregate level and simulation-based (macroscopic), such as NETVAC, DYNEV, MASSVAC, and TEVACS; most of these models were dealing with hurricanes or nuclear plant incidents, as both are among the most frequent and severe disasters in the United Stated. Few previous works exist regarding regional evacuation models on the micro simulation level In general, most of the aforementioned models are capable of estimating network clearance time and identifying evacuation bottlenecks of the network. Most of these simulators assume that the evacuation process has reached equilibrium, thereby estimating evacuation time based on determined equilibrium traffic flow. However, under abnormal situations such as evacuation, equilibrium of road traffic is hard to achieve due to the practical reason that no historical experience exists for evacuees to choose routes and minimize their evacuation time; this is contrary to normal situations recurring almost every day, in which travelers can learn from past experience to choose routes 9 with minimum travel time Disaggregate Evacuation Modeling The previously mentioned models are aggregate as they do not consider an individual's behavior while modeling the evacuation progress. Stern and Sinuany-Stern (1989) first incorporated some behavior-related parameters, including diffusion time of evacuation instruction and individuals' preparation time, in a microscopic simulation model for an urban evacuation. Later Sinuany-Stern and Stern (1993) developed the SLAM Network Evacuation Model (SNEM) based on this behavioral-based model to test the effects of traffic factors (e.g. household size, car ownership and intersection traversing time) and route choice parameters on network clearance time. Sinuany-Stern and Stern's work assumes that household members are together when a disaster occurs; therefore it takes households as entities, instead of individual household members. In reality, family members could be scattered at different places under nonotice evacuation. Murray-Tuite and Mahmassani Neighborhood Evacuation Less attention was paid to the subject of neighborhood-scale evacuation under an emergency during the last twenty years, compared to region-scale evacuation or building evacuation No-notice Evacuation Recently, more and more focus is placed on no-notice evacuation. As a no-notice disaster requires quick response, real time estimation tools are important, for which computation time is a critical issue. Chiu et al. Shelter Location Many other previous studies focused on different specific aspects of an emergency evacuation. One is that the location of shelters may influence network clearance time significantly under hurricane evacuation. Facility Location Problem This study involves relocating dependents at facilities to make an evacuation efficient, so we here provide an overview of basic facility location problems. The facility location problem is a critical issue for strategic planning of a wide range of enterprises, e.g., a retailer chooses where to locate a store or a city planner selects locations of fire stations based on a set of rules (Owen and Diskin, 1998). Basic location problems, such as the P-median, P-center, set covering and maximal covering problems, are reviewed by Owen and Diskin (1998). The P-median problem is to locate P facilities in order to minimize the total travel cost between demands and facilities; the P-Center problem, also called the minmax problem, is to locate P facilities so as to minimize the maximum travel cost between a demand and its nearest facility; the set covering problem is to locate the minimum number of facilities that will serve all demands within a specified time; the maximal covering problem is to place P facilities with the goal to maximize the amount of demand covered within an acceptable distance between demands and facilities (Owen and Diskin, 1998). The P-Center, set covering and maximal covering problems can all be applied to locate emergency 11 medical services (EMS); the P-center and maximal covering problems are used to locate a given number of EMS, and the set covering problem is used to determine the least number of EMS to cover all population of a certain area. The above mentioned basic location problems do not account for location costs, which limits their application to practical problems. The fixed charge facility location problems are thus introduced with a fixed cost for each potential location site, and categorized as uncapacitated and capacitated according to whether facility capacities are incorporated or not (Owen and Diskin, 1998). The fixed charge facility location problems determine the number of facilities located endogenously, rather than pre-specified as in median and center problems. However, without considering varying costs associated with flows between facilities and demands, fixed charge problems still cannot solve such a problem as locating a warehouse, which is a general case in industry and needs to find the best shipments between facilities and customers. Hence, the location-allocation problems incorporate flow allocation between demands and facilities into a basic location problem (usually a median problem or a fixed charge problem) (Owen and Diskin, 1998). The location problem presented in this study is essentially a P-center problem that locates students to minimize the maximum of a pickup travel time. The flow allocation is not the case of this study as it is predetermined which parent picks up which child. The previous works provide valuable contributions to the emergency evacuation field, however most of them omit family gathering behavior under no-notice evacuation conditions. This omission could lead to optimistic estimates of evacuation time. This report explores this issue and considers the fact that parents need to pick up their carless household members during an evacuation. A strategy of relocating dependents to more accessible sites to facilitate no-notice evacuation is proposed and evaluated in this report. Chapter 3 Methodology This chapter describes the methodology adopted in this study. First, an integer optimization model is formulated to determine optimal relocation sites for facilities. The microscopic simulation model is then introduced to provide zonal travel time information for the optimization model. As the two models interact with each other, iteration between them is performed to achieve the "real" optimal point. A procedure to accomplish this iteration process is illustrated by a chart and explained step by step. This chapter also includes the methodology to generate the trip chain. Optimization Model A no-notice evacuation usually starts at the moment when a disaster is confirmed or announced, then evacuees surge onto the road network in a very short time and cause traffic to dramatically change. In this study, time-dependent road traffic is taken into consideration by discretizing the evacuation period into several time intervals and capturing the travel time for each time interval. A mathematical optimization model was developed to find optimal relocation sites for facilities such as schools and daycare centers. Unlike a short-notice evacuation that seeks reductions in total evacuation time or personal property loss, a no-notice evacuation aims at maximizing the number of evacuees successfully escaping from the dangerous zones or minimizing total fatalities or injuries within a given time threshold (Chiu et al., 2006; is an index of pickup evacuees' origin nodes; j is an index of pickup evacuees' destination nodes; k is an index of current locations of facilities; l is an index of possible relocation sites for facilities; a is an index of time interval, a th ; x kl are binary integer decision variables. x kl = 1, if we assign facility k to site l; 0 otherwise; Ф is the average number of dependents a pickup evacuee gathers at a facility. Equation (1-1) calculates that the number of successful pickup evacuees for each i, j, l; p ijl S is the number of pickup evacuees (originating from i and evacuating to j) with dependent(s) relocated to l from all facilities before the last safe time interval, ijl A . Equation (1) determines the total number of successful pickup evacuees by summating p ijl S over i, j, l. Equation (2) requires the number of dependents relocated to possible site l to be no greater than facility l's capacity. The average number of dependents for a pickup evacuee is assumed to be the same over all of the facilities. Multiple intermediate stops for a pickup trip are not considered in this study; parents who have more than one dependent in the dangerous zone are assumed to have them in one facility. Equation (3) restricts the relocation site to a walkable distance (0.5 miles) from the original site. Equation (4) guarantees that a facility is assigned to one and only one relocation site. Facilities' current locations are also treated as possible relocation sites. Equation A ijl is an input to the optimization model and based on travel time from micro-simulation. Micro-simulation provides travel time of multiple paths for each OD pair; the path with the least travel time is assumed to be selected by pickup evacuees. Equations is the duration of a time interval (sec); ) ( E is the expected value of (sec). Equation Traffic Simulation Model Microscopic simulation outputs travel time among origins, destinations, and facility/relocation sites for the optimization model. Micro-simulation was chosen instead of simulation models on other levels, because it can model the road network in great detail, has the ability to model queues, and reflects the impacts of facilities' entry/exit configuration on travel time, which is crucial for the special case here. VISSIM, part of the PTV VISION traffic analysis package, was used in this study. VISSIM is a driver behavior based, second by second microscopic traffic simulation program, and developed to model major elements of transportation systems, such as lane configuration, vehicle composition, driver behavior, traffic controls and so on VISSIM's built-in dynamic traffic assignment algorithm was used to find routes for pickup evacuees. VISSIM accomplishes dynamic assignment procedures by iterated simulation runs. For each iterated run, drivers make decisions on route choice based on road traffic situations they experienced from the previous iterations. After multiple simulation runs, the iterations end when network traffic reaches stability, defined in VISSIM as when travel times or volumes do not vary significantly between two consecutive runs Framework The road traffic situation determines optimal relocation sites; reversely, relocation sites affect road traffic. In this study, relocation sites are determined by the optimization model and the network traffic is modeled using the simulation model, VISSIM. The optimization model uses travel time output from VISSIM, which assumes current facility locations as pickup points at the beginning. When the optimization model finds new relocation sites, travel time from VISSIM should be updated accordingly; as a result, these determined optimal sites may not be "real". In order to achieve "real" optimal sites, iteration between the two models is performed until convergence is reached. A procedure to accomplish this iteration process is shown in Figure 1 Flowchart of the Study In this procedure, first, the road network under normal conditions is simulated in VISSIM and normal travel times are achieved and adopted by the optimization model to be initial travel time. Then, micro simulation for emergency situations is iteratively executed with the optimization model until the termination criteria are satisfied, to determine optimal relocation sites. Facility current locations are set to be initial pickup locations; during each iteration, new relocation sites are found, and the travel time corresponding to those new sites is updated accordingly. The procedure follows the steps below: Step 0. Run VISSIM to gain travel time in a normal situation (without pickup evacuees considered). As VISSIM can only output travel time for those OD pairs with actual vehicles dispatched, we generate dummy trips for specific OD pairs to collect the travel times needed. a) Specify OD pairs we need to collect travel times for; b) Divide the simulation time period into several periods 1 ; for each period, generate one du

Link-level resilience analysis for real-world networks using crowd-sourced data

A number of recent disasters have challenged the functionality of transport networks. The significance of road transport infrastructure to the functioning means that systems need to be able to operate under undesirable conditions, and quickly return to acceptable levels of service. The objective of the study is to analyze real-world networks speed fluctuation and evaluate the quantitative relationship between resilience and graph-based metrics, and link attributes using crowd-sourced data. We measure resilience in terms of the rate (vehicle speed) at which the road network recovers from a disruptive event and define five metrics to quantify network resilience. We analyze more than 500 links affected by disruptions in multiple cities with more than millions of crowd-sourced data. Using changes in link speed before, during, and after the disruption, the resilience metrics are applied to three case studies that are categorized as no-notice disruption, notice disruption, and disruption caused by continuous events. The results indicate that link graph-based metrics and attributes have a high impact on network resilience. However, the relevance of different metrics and attributes to the link resilience is different. Population density, predictability of disasters, and human factors have a significant impact on the reduction and recovery phases

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Analysis of Transportation Networks Subject To Natural Hazards – insights from a Colombian case

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Health System Resource Gaps and Associated Mortality from Pandemic Influenza across Six Asian Territories

BACKGROUND: Southeast Asia has been the focus of considerable investment in pandemic influenza preparedness. Given the wide variation in socio-economic conditions, health system capacity across the region is likely to impact to varying degrees on pandemic mitigation operations. We aimed to estimate and compare the resource gaps, and potential mortalities associated with those gaps, for responding to pandemic influenza within and between six territories in Asia. METHODS AND FINDINGS: We collected health system resource data from Cambodia, Indonesia (Jakarta and Bali), Lao PDR, Taiwan, Thailand and Vietnam. We applied a mathematical transmission model to simulate a "mild-to-moderate" pandemic influenza scenario to estimate resource needs, gaps, and attributable mortalities at province level within each territory. The results show that wide variations exist in resource capacities between and within the six territories, with substantial mortalities predicted as a result of resource gaps (referred to here as "avoidable" mortalities), particularly in poorer areas. Severe nationwide shortages of mechanical ventilators were estimated to be a major cause of avoidable mortalities in all territories except Taiwan. Other resources (oseltamivir, hospital beds and human resources) are inequitably distributed within countries. Estimates of resource gaps and avoidable mortalities were highly sensitive to model parameters defining the transmissibility and clinical severity of the pandemic scenario. However, geographic patterns observed within and across territories remained similar for the range of parameter values explored. CONCLUSIONS: The findings have important implications for where (both geographically and in terms of which resource types) investment is most needed, and the potential impact of resource mobilization for mitigating the disease burden of an influenza pandemic. Effective mobilization of resources across administrative boundaries could go some way towards minimizing avoidable deaths

ADAMS project: a genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis based in the UK

PURPOSE: Genetic studies of multiple sclerosis (MS) susceptibility and severity have focused on populations of European ancestry. Studying MS genetics in other ancestral groups is necessary to determine the generalisability of these findings. The genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis (ADAMS) project aims to gather genetic and phenotypic data on a large cohort of ancestrally-diverse individuals with MS living in the UK. PARTICIPANTS: Adults with self-reported MS from diverse ancestral backgrounds. Recruitment is via clinical sites, online (https://app.mantal.co.uk/adams) or the UK MS Register. We are collecting demographic and phenotypic data using a baseline questionnaire and subsequent healthcare record linkage. We are collecting DNA from participants using saliva kits (Oragene-600) and genotyping using the Illumina Global Screening Array V.3. FINDINGS TO DATE: As of 3 January 2023, we have recruited 682 participants (n=446 online, n=55 via sites, n=181 via the UK MS Register). Of this initial cohort, 71.2% of participants are female, with a median age of 44.9 years at recruitment. Over 60% of the cohort are non-white British, with 23.5% identifying as Asian or Asian British, 16.2% as Black, African, Caribbean or Black British and 20.9% identifying as having mixed or other backgrounds. The median age at first symptom is 28 years, and median age at diagnosis is 32 years. 76.8% have relapsing-remitting MS, and 13.5% have secondary progressive MS. FUTURE PLANS: Recruitment will continue over the next 10 years. Genotyping and genetic data quality control are ongoing. Within the next 3 years, we aim to perform initial genetic analyses of susceptibility and severity with a view to replicating the findings from European-ancestry studies. In the long term, genetic data will be combined with other datasets to further cross-ancestry genetic discoveries

Trends in parameterization, economics and host behaviour in influenza pandemic modelling: a review and reporting protocol.

BACKGROUND: The volume of influenza pandemic modelling studies has increased dramatically in the last decade. Many models incorporate now sophisticated parameterization and validation techniques, economic analyses and the behaviour of individuals. METHODS: We reviewed trends in these aspects in models for influenza pandemic preparedness that aimed to generate policy insights for epidemic management and were published from 2000 to September 2011, i.e. before and after the 2009 pandemic. RESULTS: We find that many influenza pandemics models rely on parameters from previous modelling studies, models are rarely validated using observed data and are seldom applied to low-income countries. Mechanisms for international data sharing would be necessary to facilitate a wider adoption of model validation. The variety of modelling decisions makes it difficult to compare and evaluate models systematically. CONCLUSIONS: We propose a model Characteristics, Construction, Parameterization and Validation aspects protocol (CCPV protocol) to contribute to the systematisation of the reporting of models with an emphasis on the incorporation of economic aspects and host behaviour. Model reporting, as already exists in many other fields of modelling, would increase confidence in model results, and transparency in their assessment and comparison

Transcriptional and Linkage Analyses Identify Loci that Mediate the Differential Macrophage Response to Inflammatory Stimuli and Infection

Macrophages display flexible activation states that range between pro-inflammatory (classical activation) and anti-inflammatory (alternative activation). These macrophage polarization states contribute to a variety of organismal phenotypes such as tissue remodeling and susceptibility to infectious and inflammatory diseases. Several macrophage- or immune-related genes have been shown to modulate infectious and inflammatory disease pathogenesis. However, the potential role that differences in macrophage activation phenotypes play in modulating differences in susceptibility to infectious and inflammatory disease is just emerging. We integrated transcriptional profiling and linkage analyses to determine the genetic basis for the differential murine macrophage response to inflammatory stimuli and to infection with the obligate intracellular parasite Toxoplasma gondii. We show that specific transcriptional programs, defined by distinct genomic loci, modulate macrophage activation phenotypes. In addition, we show that the difference between AJ and C57BL/6J macrophages in controlling Toxoplasma growth after stimulation with interferon gamma and tumor necrosis factor alpha mapped to chromosome 3, proximal to the Guanylate binding protein (Gbp) locus that is known to modulate the murine macrophage response to Toxoplasma. Using an shRNA-knockdown strategy, we show that the transcript levels of an RNA helicase, Ddx1, regulates strain differences in the amount of nitric oxide produced by macrophage after stimulation with interferon gamma and tumor necrosis factor. Our results provide a template for discovering candidate genes that modulate macrophage-mediated complex traits

Parallels between Pathogens and Gluten Peptides in Celiac Sprue

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