81 research outputs found
AppendixA.rjf_online_supp – Supplemental material for Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
No full textSupplemental material, AppendixA.rjf_online_supp for Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing by Reed Harder, Keji Wei, Vikrant Vaze and James E. Stahl in MDM Policy & Practice</p
AppendixB_online_supp – Supplemental material for Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing
No full textSupplemental material, AppendixB_online_supp for Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing by Reed Harder, Keji Wei, Vikrant Vaze and James E. Stahl in MDM Policy & Practice</p
Modeling Airline Frequency Competition for Airport Congestion Mitigation
No full textDemand often exceeds capacity at congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. We propose a game-theoretic model for airline frequency competition under slot constraints. The model is solved to obtain a Nash equilibrium using a successive optimizations approach, wherein individual optimizations are performed using a dynamic programming-based technique. The model predictions are validated against actual frequency data, with the results indicating a close fit to reality. We use the model to evaluate different strategic slot allocation schemes from the perspectives of the airlines and the passengers. The most significant result of this research shows that a small reduction in the total number of allocated slots translates into a substantial reduction in flight and passenger delays and also a considerable improvement in airlines' profits
Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 173-180).Accurate calibration of demand and supply simulators within a Dynamic Traffic Assignment (DTA) system is critical for the provision of consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as Automatic Vehicle Identification (AVI) technology provide a rich source of disaggregate traffic data. This thesis presents a methodology for calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic sensing technologies. The calibration problem has been formulated in two different frameworks, viz. in a state-space framework and in a stochastic optimization framework. Three different algorithms are used for solving the calibration problem, a gradient approximation based path search method (SPSA), a random search meta-heuristic (GA) and a Monte-Carlo simulation based technique (Particle Filter). The methodology is first tested using a small synthetic study network to illustrate its effectiveness. Later the methodology is applied to a real traffic network in the Lower Westchester County region in New York to demonstrate its scalability.(cont.) The estimation results are tested using a calibrated Microscopic Traffic Simulator (MITSIMLab). The results are compared to the base case of calibration using only the conventional point sensor data. The results indicate that the utilization of AVI data significantly improves the calibration accuracy.by Vikrant Vaze.S.M
Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
No full textAccurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy
Competition and congestion in the US NAS : multi-agent, multi-stakeholder approaches for evaluation and mitigation
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 273-280).The US National Aviation System (NAS) is a complex system with multiple, interacting agents including airlines, passengers, and system operators, each with somewhat different objectives and incentives. These interactions determine the state of the system. NAS congestion and delays result in additional operating costs and reduced profitability for the airlines, a decrease in the level-of-service to passengers, and a decrease in the efficiency of NAS resource utilization. We evaluate the congestion impacts on the NAS stakeholders while explicitly accounting for their interactions and propose congestion mitigation mechanisms that are beneficial to these different stakeholders. We measure the extent to which the NAS capacity is being inefficiently utilized. We show that at the current level of passenger demand, delays are avoidable to a large extent if we control the negative effects of competitive airline scheduling practices, thus providing critical insights into the nature and causes of delays. We develop a detailed framework using data fusion and discrete choice modeling' for generating disaggregate passenger travel data. We characterize the impacts of airline network structures, schedules and operational decisions on passenger delays. We propose a parametric game-theoretic model consistent with the most popular characterization of frequency competition. We prove that the level of congestion in a system of competing airlines is an increasing function of 1) the number of competing airlines, 2) a measure of the gross profit margin, and 3) the frequency sensitivity of passenger demand. We propose a game-theoretic model of frequency competition under slot constraints and provide empirical and algorithmic justifications of the suitability of the Nash equilibrium solution concept for modeling these games. We devise and assess new administrative strategies for congestion mitigation. We show that a small reduction in the total number of allocated slots translates into a substantial reduction in delays, and also a considerable improvement in airlines' profits. We develop an equilibrium model of frequency competition in the presence of delay costs and congestion prices. We find that the success of congestion pricing critically depends on the characteristics of frequency competition in individual markets. We also identify critical differences between flat pricing and marginal cost pricing. Key words: Airline Scheduling, Airline Frequency Competition, National Aviation System, Stakeholders, Multi-agent Models, Nash Equilibrium, Game Theory, Price of Anarchy, Passenger Delays, Cancellations, Missed Connections, Cost of Passenger Disruptions, Administrative Slot Controls, Slot Reduction, Congestion Pricing.by Vikrant Suhas Vaze.Ph.D
Integrated schedule recovery: a multi-agent approach
No full textTese no âmbito do Programa Doutoral em Sistemas de Transportes apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraDelays and disruptions in airline operations annually result in billions of dollars of additional costs to airlines, passengers, and the economy. Airlines strive to mitigate these costs by creating schedules that are less likely to get disrupted or schedules that are easier to repair upon disruptions. New types of aircraft, larger fleets, expanding networks and increasing constraints on crew scheduling from regulators, crew collective bargains, and decreased fares, force airlines to make their operations more efficient. The presence of considerable uncertainty makes this already complex problem faced by the airlines even harder to solve. The air transportation industry has long tradition of using operations research techniques to solve their problems. To cope with their problems the airline industry has used optimization models in one form or another. Hence schedules are trimmed to eliminate buffers and slacks, but this can lead to greater propagation of delays, even due to minor incidents. These intricate problems are hard to understand and all its interactions are virtually impossible to grasp in a purely closed-form mathematical approach.
We present in this thesis several methodological approaches to help researchers and industry practitioners dealing with uncertainty in airline scheduling.
First, we present a robust optimization model for the crew pairing problem, which generates crew schedules that are less likely to get disrupted. Our model allows adding robustness without requiring detailed knowledge of the underlying delay distributions. Moreover, our model allows to capture in detail the delay propagation through crew connections and the complex cost structure of the pay-and-credit crew salary scheme, thus enabling us to find a good tradeoff between the planned costs on one hand and the expected delay and disruption costs on the other hand. Our robust crew pairing model is based on a deterministic crew pairing model formulated as a mixed-integer linear program. The robust version that we propose retains the linearity of the constraints and objective function, and thus can be handled by commercial solvers, which facilitates its implementation in practice. We propose and implement a new solution algorithm for solving our model to optimality. Several optimal solutions with varying robustness levels are compared for the network of a moderate-sized airline in the United States. We test the model’s solutions in a simulation environment using real-world delay data. Our simulation results show that the robust crew pairing solutions lead to lower delays and fewer instances of operational infeasibilities, thus requiring fewer recovery actions to address them. We conclude that, with the inclusion of robustness, it is possible to generate crew pairing solutions that significantly reduce the delay and disruption costs with only a small increase in planned costs.
Second, we present a new stochastic simulation platform that is able to simulate the daily operations of an airline allowing industry analysts, practitioners and researchers to evaluate the behavior of an airline network under uncertainty. This simulation platform was developed specifically to be user-friendly and require moderate input needs. We demonstrate the adequacy of an agent-based approach to model a complex system such as an airline. Additionally, we demonstrate that an accurate representation of an airline’s operation needs to explicitly consider the airline’s response to uncertainty and disruptions, by accurately modeling the airline as a decision-maker in face of uncertainty. This is demonstrated through a detailed case study where we compare two recovery models that, due to their different objectives, lead to different behaviors of the simulated airline. We demonstrate and compare the tradeoffs between cancelations and flight delays that are made by these models to reach to their objectives.
Third, we present a machine learning metamodel approach where we leverage information extracted from the historic data, from previous schedule recovery problems solved by optimization models, to generate immediate solutions for the schedule recovery problem. In our proof of concept case study, we use an artificial neural network that, using historic data of solutions and problems, is able to extract the solution pattern and return the problem solution without needing to re-run the optimization model. We show that it is feasible to use machine learning metamodels to predict solutions of optimization models based on historic data. With an acceptable level of accuracy, the metamodel was able to predict the solution of a recovery optimization modelAtrasos e perturbações nas operações resultam anualmente em milhares de milhões de dólares de custos adicionais para as companhias aéreas, para os seus passageiros e para a economia em geral. As companhias aéreas tentam mitigar esses custos criando horários com menos probabilidade de serem perturbados. Novos desafios devidos a novos tipos de aeronaves, à expansão de redes, ao aumento das restrições aos horários das tripulações por parte de reguladores e contratos coletivos e à diminuição de tarifas, forçam as companhias aéreas a tornarem as suas operações mais eficientes. A presença de incerteza torna a gestão das operações de companhias aéreas ainda mais complexa. O setor do transporte aéreo tem uma longa tradição de usar técnicas de investigação operacional para resolver os seus problemas operacionais. Neste sentido, os horários são otimizados para reduzir margens, o que pode levar a uma maior propagação de atrasos. São problemas complexos e de difícil compreensão difíceis de traduzir adequadamente por modelos matemáticos.
Apresentamos várias abordagens para ajudar os investigadores e profissionais da indústria a lidar com a incerteza na gestão operacional de companhias aéreas. Primeiro, apresentamos um modelo de otimização robusto para o problema do emparelhamento de tripulações, que gera horários para as tripulações com menos probabilidade de serem afetados por incerteza. O modelo permite adicionar robustez sem exigir um conhecimento detalhado das distribuições subjacentes aos atrasos. Além disso, o modelo permite capturar pormenorizadamente a propagação dos atrasos decorrentes das conexões da tripulação e a complexa estrutura de custos dos salários dos membros das tripulações, permitindo-nos encontrar um bom equilíbrio entre os custos planeados e os custos adicionais relativos a atrasos e perturbações das operações. O modelo robusto de emparelhamento de tripulação proposto é baseado num determinístico formulado como um problema inteiro misto. A versão robusta que propomos mantém a linearidade das restrições e da função objetivo e, portanto, pode ser tratada por softwares comerciais, o que facilita a sua implementação prática. Propomos e implementamos um novo algoritmo para obter soluções ótimas globais para o modelo. Soluções ótimas correspondentes a vários níveis de robustez são comparadas para uma companhia aérea de tamanho moderado que opera nos Estados Unidos. Testamos as soluções do modelo em um ambiente de simulação usando dados de atrasos reais. Os resultados da simulação mostram que as soluções robustas de emparelhamento de tripulações levam a atrasos menores e menos ocorrências de inviabilidade operacional, exigindo menos ações de recuperação para as resolver. Verificámos que, com a inclusão da robustez, é possível gerar soluções de emparelhamento de tripulações que reduzem significativamente os custos com atrasos e perturbações das operações com apenas um pequeno aumento nos custos planeados.
Em segundo lugar, apresentamos uma nova plataforma de simulação estocástica que é capaz de simular as operações diárias de uma companhia aérea, permitindo que analistas, profissionais e pesquisadores do setor avaliem o comportamento da rede de uma companhia aérea sob condições de incerteza. Demonstramos a adequação de uma abordagem baseada em agentes para modelar um sistema complexo como uma companhia aérea. Além disso, demonstramos que uma representação adequada da operação de uma companhia aérea carece da consideração explícita da respetiva resposta a incertezas e perturbações. O funcionamento da plataforma é exemplificado por meio de um estudo de caso detalhado em que comparamos dois modelos de recuperação de horários de companhias aéreas que, devido terem objetivos distintos, levam a diferentes comportamentos da companhia aérea simulada. Determinamos e comparamos os trade-offs entre cancelamentos e atrasos de voo que são feitos por esses modelos em função dos objetivos da companhia.
Em terceiro lugar, apresentamos uma meta-modelo “machine learning” onde aproveitamos informações extraídas dos dados históricos de problemas de recuperação de horários de companhias aéreas resolvidos por modelos de otimização para gerar soluções imediatas. No estudo de caso que desenvolvemos como prova de conceito, usamos uma rede neuronal artificial que, a partir de dados históricos de soluções e problemas, é capaz de extrair o padrão da solução e gerar a solução do problema sem precisar de resolver novamente o modelo de otimização. Mostramos que é possível usar meta-modelos de “machine learning” para prever soluções de modelos de otimização baseados em dados históricos com um nível aceitável de precisão. O meta-modelo conseguiu prever a solução de um modelo de otimização de recuperação de horários
Securitization and mortgage default
No full textThe academic literature, the popular press, and policymakers have all debated securitization's contribution to the poor performance of mortgages originated in the run-up to the recent crisis. Theoretical arguments have been advanced on both sides, but the lack of suitable data has made it difficult to assess them empirically. The author examines this issue by using a loan-level data set from LPS Analytics, covering approximately two-thirds of the mortgages originated in 2005 and 2006, and including both securitized and nonsecuritized loans. ; The author finds evidence that privately securitized loans do indeed perform worse than observably similar, nonsecuritized loans. Moreover, this effect is strongest in prime mortgage markets, which have not been studied in the previous literature. For example, a typical prime loan becomes delinquent at a 20 percent higher rate if it is privately securitized, ceteris paribus. This is consistent with the existence of adverse selection; that is, that lenders used information not available to investors to securitize loans that were riskier than they otherwise appeared. By contrast, for subprime mortgages, the impact of private securitization is concentrated in low or no-documentation loans; this latter result is consistent with previous work such as Keys et al. (2009).Mortgage-backed securities ; Default (Finance)
Integrated Transit-Parking Planning
No full textTese de doutoramento em Sistemas de Transporte, apresentada ao Departamento de Engenharia Civil da Faculdade de Ciências e Tecnologia da Universidade de CoimbraPublic transit systems are not only essential for urban mobility but are also advantageous from the fuel consumption, pollutant emissions and traffic congestion standpoints. In addition to this, transit also provides an alternative with acceptable levels of mobility to people who cannot own or drive a car. In fact, the main goal of having a transit system is to offer good quality service, where users travel easily at a low fare while reducing pollution and traffic congestion. This goal often results in serious financial problems for the transit operators, as their revenues are rarely enough to cover their expenses, requiring subsidies funded by local governments. In this context, we propose the integration of transit and parking systems as an option to decrease the subsidies of transit systems. This integration is developed considering two different views. A physical integration of the two systems; and an integration through prices (transit fares and parking fees), where two different standpoints are considered. One that assumes a regulated market, where the parking operator revenues will be used to fund the transit operator deficits; and another that assumes a fully deregulated market, where both transit operators and parking operators have a profit maximization goal.
The physical integration of the two systems was illustrated through an optimization-based study carried out for Coimbra (Portugal), with the goal of selecting the best locations for park-and-ride facilities so that car use inside the city is minimized. Park-and-ride facilities are parking lots located in the periphery of cities to intercept car trips coming from the suburbs, and divert them to transit. In this study, the transport mode choices were assumed to be dependent on the generalized travel costs of car, transit and park-and-ride according to a logit function. The main result was that the introduction of a park-and-ride network could reduce car use in Coimbra’s city center by 19%.
The integration of transit and parking systems through prices in a regulated market was approached with an optimization model, where transit and parking are managed together to minimize the joint deficit of the respective operators, considering transit fares and parking fees as decision variables. The context of application of this model is a city divided into zones, where trips between each pair of zones can be made either by car or by bus, or not made if (generalized) travel costs are considered too high by the traveler. Modal choice in the city is described by a logit model of the generalized travel costs of both modes. In the case of car, these costs consist of vehicle depreciation, fuel, maintenance, travel time and parking fees, while time costs, discomfort costs and transit fares are the costs included in the transit generalized travel cost. This model was applied to a case study in Coimbra, where both transit and parking systems become clearly profitable due to a substantial increase of prices. However, the relationship between demand and speed is not addressed in this model, as it is assumed that speed values remain unchanged even when modal choices change.
This shortcoming was handled by embedding on the optimization model a network level aggregate traffic model based on the macroscopic fundamental diagram (MFD), which determines the speeds and cruising-for-parking costs considering car travel demand. Due to the complexity of the optimization model, a solution method based on a traffic-equilibrium algorithm and a greedy algorithm was developed. Through the application of a case study inspired by the city of Coimbra, it was possible to verify that the joint operating deficits were decreased, leading to a profitable transit system.
An alternative SA algorithm was also developed in view of its future application to solve the previous model. If properly designed, algorithms of this type show good global optimum convergence properties. Otherwise, the quality of the best solution they return may be low or the computation time they require may be excessively long. The reason for this to happen may be because SA algorithms spend too much effort evaluating poor quality solutions. To avoid this, we hybridize a cross-entropy algorithm with a SA algorithm, in order to decrease the probability that a low-quality candidate solution is selected in each iteration. The results of a computational study developed for a facility location problem indicate that the hybrid algorithm clearly improves the classic SA algorithm.
The integration of transit and parking systems under a deregulated market was handled through a two-stage game-theoretic approach, assuming transit and parking operator as profit maximizers. The first stage decisions are parking capacity, transit frequencies and fleet size, whereas pricing decisions are made in the second stage, assuming the first-stage decisions known and fixed. The concept of subgame-perfect pure strategy Nash equilibrium was used to solve this game. By analyzing several hypothetical case studies (inspired by real-world situations), it was shown how the decisions of the operators are expected to interact.
In general, the proposed models and their applications contribute what we believe to be a significant addition to the literature. These integrated transit-parking planning models provide a better understanding of how park-and-ride networks and pricing schemes affect the city’s mobility dynamics and modal choices, and insight into the impact of the decisions of transit and parking operators on their financial performance.Os sistemas de transportes públicos são não só essenciais à mobilidade urbana, mas também vantajosos em relação ao automóvel quanto ao consumo de combustível, emissão de poluentes e congestionamento do tráfego. Adicionalmente, os transportes públicos são uma opção que garante níveis aceitáveis de mobilidade a quem não conduz ou não tem automóvel. De facto, o principal objetivo de um sistema de transportes públicos é providenciar um serviço de qualidade através do qual os seus utilizadores possam viajar a custo relativamente baixo e, simultaneamente, contribuir para a diminuição da poluição e do congestionamento. A prossecução deste objetivo origina geralmente sérios problemas financeiros para os operadores de transportes públicos, uma vez que as receitas não são, em regra, suficientes para cobrir os custos do sistema, o que requer a subsidiação por entidades públicas. É neste contexto que analisamos a integração de sistemas de transportes públicos e de estacionamento como uma possibilidade para diminuir os subsídios dos transportes públicos. Esta integração dos dois sistemas é estudada de duas perspetivas distintas – integração física e integração através dos preços (dos bilhetes de transporte público e de tarifas de estacionamento) – e segundo dois pontos de vista diferentes: um que assume um mercado regulado, no qual as receitas do estacionamento são utilizadas para financiar os défices dos transportes públicos; e outro que assume um mercado totalmente desregulado, em que tanto o operador de transportes públicos como o operador do estacionamento têm como objetivo a maximização do lucro.
A integração física dos dois sistemas é analisada tendo por referência um estudo de otimização desenvolvido para Coimbra (Portugal), com o objetivo de selecionar localizações para estacionamentos park-and-ride que minimizem a utilização de automóveis no centro das cidades. Os estacionamentos park-and-ride localizam-se na periferia das cidades com o objetivo de intercetar as viagens de automóvel que vêm dos subúrbios. Neste estudo, assume-se que as escolhas modais dependem dos custos generalizados de viagem por automóvel, por transportes públicos ou pelos dois modos através de um parque de estacionamento periférico, de acordo com uma função logit. O principal resultado que obtivemos com a introdução de estacionamentos park-and-ride foi a redução do uso do automóvel no centro de Coimbra em 19%.
A integração de transportes públicos e estacionamento através de preços num mercado regulado foi analisada com base em um modelo de otimização no qual os transportes públicos e o estacionamento são geridos em conjunto, a fim de diminuir o seu défice global, considerando os preços dos bilhetes e as tarifas de estacionamento como variáveis de decisão. O contexto para a aplicação deste modelo é uma cidade dividida em zonas, onde as viagens correspondentes a cada par origem-destino podem ser feitas ou de automóvel ou de transportes públicos, ou não ser realizadas caso o seu custo generalizado seja considerado muito elevado. A escolha do modo de transportes é descrita por um modelo logit dos custos generalizados dos vários modos. No caso do automóvel, estes custos contemplam a depreciação do veículo, o combustível, a manutenção, o tempo de viagem e a tarifa de estacionamento, enquanto o tempo de viagem, o desconforto e o preço do bilhete são contabilizados nos custos generalizados de uma viagem em transportes públicos. Este modelo foi aplicado ao estudo de caso de Coimbra, concluindo-se que ambos os sistemas se poderiam tornar bastante lucrativos como resultado de um aumento substancial de preços. Contudo, a relação entre volumes de tráfego e velocidades de circulação não foi tratada de forma apropriada neste modelo, pois que se considerou que aquelas velocidades permaneceriam constantes independentemente das escolhas modais.
Esta lacuna foi ultrapassada através da inclusão, no modelo de otimização, de um modelo de tráfego agregado a nível de rede baseado no denominado diagrama fundamental, que determina as velocidades de circulação e os níveis de cruising-for-parking tendo em conta a procura de viagens de automóvel. Dada a complexidade do modelo, foi desenvolvido um método para o resolver baseado na combinação de um algoritmo de equilíbrio de tráfego com uma heurística de tipo greedy. A respetiva aplicação ao caso de Coimbra permitiu concluir que seria possível tornar o sistema de transportes públicos lucrativo.
Uma heurística alternativa baseada num algoritmo de simulated annealing (SA) foi também desenvolvida para futura resolução do modelo anteriormente apresentado. Os algoritmos SA apresentam boas propriedades de convergência para um ótimo global, mas podem tornar-se muito lentos se se quiser garantir soluções de boa qualidade. Essa lentidão decorre do facto do algoritmo passar muito tempo a analisar soluções de baixa qualidade. Para contornar este problema, hibridizámos um algoritmo de cross entropy com um algoritmo SA. Os resultados obtidos através de um estudo computacional desenvolvido para um problema de localização de equipamentos indicam que o algoritmo híbrido melhora claramente a performance do algoritmo SA clássico.
A integração de transportes públicos e estacionamento num mercado totalmente desregulado foi analisada através de uma abordagem por teoria dos jogos com dois estádios. O primeiro estádio tem como decisões a capacidade de estacionamento, a frequência dos transportes públicos e a dimensão da frota, ao passo que as decisões referentes aos preços são tomadas num segundo estádio, onde são assumidas como conhecidas e fixas as decisões tomadas no primeiro estádio. Cada estádio do jogo foi resolvido tendo em conta o conceito de equilíbrio de Nash. Em diversos estudos de caso hipotéticos (inspirados em situações reais) é examinada a forma como se dá a interação entre as decisões do operador de transporte público e do operador de estacionamento.
Em geral, acreditamos que os modelos propostos e as suas aplicações contribuem de forma significativa para a literatura. Os modelos em causa permitem apoiar as entidades responsáveis pelo planeamento de transportes públicos e do estacionamento, contribuindo para que as decisões que tomem sejam mais eficientes
Role of Risk Stratification and Genetics in Sudden Cardiac Death
No full textSudden cardiac death (SCD) is a major public health issue due to its increasing incidence in the general population and the difficulty in identifying high-risk individuals. Nearly 300,000-350,000 patients in the United States and 4- to 5 million patients in the world die from SCD. Coronary artery disease and advanced heart failure are the main etiology for SCD. Ischemia of any cause precipitates lethal arrhythmias, and ventricular tachycardia and ventricular fibrillation are the most common lethal arrhythmias precipitating SCD. Pulse-less electrical activity, brady-arrhythmia and electromechanical dissociation also result in SCD. Most sudden cardiac deaths occur out-of-the-hospital setting, so it is difficult to estimate the public burden, which results in overestimating the incidence of SCD. The insufficiency and limited predictive value of various indicators and criteria for SCD result in the increasing incidences. As a result, there is a need to develop better risk stratification criteria and find modifiable variables to decrease the incidence. Primary and secondary prevention and treatment of SCD need further research. This critical review is focused on the etiology, risk factors, prognostic factors and importance of risk stratification of SCD.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
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