Optimizing Crowd Evacuation In The Emergency Route Planning Problem

Situasi bencana, yang berlaku secara semula jadi (kebakaran, banjir, taufan) atau buatan manusia (contohnya pengeboman pengganas, tumpahan bahan kimia, dan lain-lain), telah meragut ribuan nyawa, mencetuskan keperluan untuk pemindahan kecemasan. Biasanya, mengoptimumkan pelan pemindahan kecemasan melibatkan berkesanan pemodelan orang ramai dan pemilihan laluan, dimana pelan yang optimum penting dalam masalah perancangan laluan kecemasan (ERP). Pelbagai pendekatan ERP telah dibangunkan dimana diklasifikasikan kepada pendekatan matematik, keputusan sokongan, heuristik, dan meta-heuristik. Ulasan kesusasteraan menyeluruh telah menunjukkan kepentingan untuk merapatkan jurang antara pemodelan dan pemilihan laluan, di mana di mana pendekatan bersepadu dan berdaya maju diperlukan. Dalam kajian ini, satu perancangan pemindahan rangka kerja bersepadu menggunakan model pemindahan orang ramai dan sistem imun (AIS) algoritma tiruan, yang dipanggil iEvaP, telah dicadangkan. iEvaP telah disahkan terhadap Lu et al. (2003) dan parameternya telah ditentukan untuk prestasi yang optimum. Disastrous situations, either natural (e.g. fires, floods, hurricane) or man-made (e.g. terrorist bombings, chemical spills, etc.), have claimed the lives of thousands, triggering the needs for emergency evacuation. Typically, optimizing an emergency evacuation plan involves both the effectiveness in crowd modelling and route selection, where an optimum evacuation plan is vital in the emergency route planning (ERP) problem. Various ERP approaches have been developed which are classified into mathematical, decision-support, heuristic, and meta-heuristic approaches. Exhaustive literature reviews have shown the significance of bridging the gap between modeling and routing, where an integrated and viable approach is needed. In this study, an integrated evacuation planning framework utilizing crowd evacuation model and an artificial immune system (AIS) algorithm, called iEvaP, was proposed. iEvaP was validated against Lu et al. (2003) and its parameters were calibrated for optimum performance

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

EVACUAÇÃO DE MULTIDÕES EM SITUAÇÃO DE EMERGÊNCIA

Existem vários tipos de eventos que podendo ser de caracter social, recreativo, desportivo, político ou religioso reúnem num determinado local um grande número de pessoas. O comportamento das pessoas nestes locais é consideravelmente diferente, em especial quando ocorre um incidente crítico. A evacuação de locais com uma grande concentração de público tem sido objeto de diversos estudos, procurando compreender a dinâmica das pessoas de forma a prever o seu comportamento em emergência, mais especificamente, no que diz respeito à análise do risco. Identificar os pontos críticos e onde podem ocorrer desastres é crucial na gestão de emergência, pelo que os modelos existentes têm possibilitado a compreensão de fenómenos associados à evacuação. Este artigo baseia-se numa revisão de literatura, pretendendo melhorar a compreensão dos fenómenos associados ao processo de evacuação em reuniões de massas, destacando algumas lacunas de pesquisa no contexto da segurança.info:eu-repo/semantics/publishedVersio

Reuniões de massas - Fenómenos e modelos de evacuação

Reuniões em massa, fazem parte do dia a dia e podem ocorrer em diferentes lugares, dependendo das especificidades do evento. Devido à baixa perceção de risco as pessoas tendem a subestimar a probabilidade de ocorrência de um desastre e as suas consequências negativas, no entanto a história mundial evidência que os tumultos nestas reuniões ocorrem frequentemente, sendo necessário estudar o comportamento da multidão. Este pressuposto permitiu desenvolver um trabalho de pesquisa através de revisão de literatura, analisando de forma abrangente os estudos existentes. A evacuação em larga escala é uma medida eficaz para mitigar o risco numa emergência, no entanto, sob a perspetiva da segurança apresenta um conjunto de desafios devido ao comportamento psicomotor do individuo interferir em todo o processo. Depois de décadas de modelos teóricos e onde a evacuação se limitava à análise prévia da arquitetura do espaço e ao número de sobreviventes, a simulação computacional tornou-se uma ferramenta padrão para planear e avaliar a evacuação de multidões, no entanto, muitos modelos são estruturalmente diferentes e poucos foram rigorosamente testados. A grande vantagem é a antevisão de diversos cenários, sem colocar em risco os indivíduos, verifica-se, no entanto, que reproduzir emoções, comportamentos experiências e conhecimentos é muito difícil.info:eu-repo/semantics/publishedVersio

Navigation, Path Planning, and Task Allocation Framework For Mobile Co-Robotic Service Applications in Indoor Building Environments

Recent advances in computing and robotics offer significant potential for improved autonomy in the operation and utilization of today’s buildings. Examples of such building environment functions that could be improved through automation include: a) building performance monitoring for real-time system control and long-term asset management; and b) assisted indoor navigation for improved accessibility and wayfinding. To enable such autonomy, algorithms related to task allocation, path planning, and navigation are required as fundamental technical capabilities. Existing algorithms in these domains have primarily been developed for outdoor environments. However, key technical challenges that prevent the adoption of such algorithms to indoor environments include: a) the inability of the widely adopted outdoor positioning method (Global Positioning System - GPS) to work indoors; and b) the incompleteness of graph networks formed based on indoor environments due to physical access constraints not encountered outdoors. The objective of this dissertation is to develop general and scalable task allocation, path planning, and navigation algorithms for indoor mobile co-robots that are immune to the aforementioned challenges. The primary contributions of this research are: a) route planning and task allocation algorithms for centrally-located mobile co-robots charged with spatiotemporal tasks in arbitrary built environments; b) path planning algorithms that take preferential and pragmatic constraints (e.g., wheelchair ramps) into consideration to determine optimal accessible paths in building environments; and c) navigation and drift correction algorithms for autonomous mobile robotic data collection in buildings. The developed methods and the resulting computational framework have been validated through several simulated experiments and physical deployments in real building environments. Specifically, a scenario analysis is conducted to compare the performance of existing outdoor methods with the developed approach for indoor multi-robotic task allocation and route planning. A simulated case study is performed along with a pilot experiment in an indoor built environment to test the efficiency of the path planning algorithm and the performance of the assisted navigation interface developed considering people with physical disabilities (i.e., wheelchair users) as building occupants and visitors. Furthermore, a case study is performed to demonstrate the informed retrofit decision-making process with the help of data collected by an intelligent multi-sensor fused robot that is subsequently used in an EnergyPlus simulation. The results demonstrate the feasibility of the proposed methods in a range of applications involving constraints on both the environment (e.g., path obstructions) and robot capabilities (e.g., maximum travel distance on a single charge). By focusing on the technical capabilities required for safe and efficient indoor robot operation, this dissertation contributes to the fundamental science that will make mobile co-robots ubiquitous in building environments in the near future.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143969/1/baddu_1.pd

Developing an agent-based evacuation simulation model based on the study of human behaviour in fire investigation reports

Fire disasters happen every day all over the world. These hazardous events threaten people's lives and force an immediate movement of people wanting to escape from a dangerous area. Evacuation drills are held to encourage people to practise evacuation skills and to ensure they are familiar with the environment. However, these drills cannot accurately represent real emergency situations and, in some cases, people may be injured during practice. Therefore, modelling pedestrian motion and crowd dynamics in evacuation situations has important implications for human safety, building design, and evacuation processes. This thesis focuses on indoor pedestrian evacuation in fire disasters. To understand how humans behave in emergency situations, and to simulate more realistic human behaviour, this thesis studies human behaviour from fire investigation reports, which provide a variety details about the building, fire circumstance, and human behaviour from professional fire investigation teams. A generic agent-based evacuation model is developed based on common human behaviour that indentified in the fire investigation reports studied. A number of human evacuation behaviours are selected and then used to design different types of agents, assigning with various characteristics. In addition, the interactions between various agents and an evacuation timeline are modelled to simulate human behaviour and evacuation phenomena during evacuation. The application developed is validated using three specific real fire cases to evaluate how closely the simulation results reflected reality. The model provides information on the number of casualties, high-risk areas, egress selections, and evacuation time. In addition, changes to the building configuration, number of occupants, and location of fire origin are tested in order to predict potential risk areas, building capacity and evacuation time for different situations. Consequently, the application can be used to inform building designs, evacuation plans, and priority rescue processes

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment