Exitus: An Agent-Based Evacuation Simulation Model For Heterogeneous Populations

Evacuation planning for private-sector organizations is an important consideration given the continuing occurrence of both natural and human-caused disasters that inordinately affect them. Unfortunately, the traditional management approach that is focused on fire drills presents several practical challenges at the scale required for many organizations but especially those responsible for national critical infrastructure assets such as airports and sports arenas. In this research we developed Exitus, a comprehensive decision support system that may be used to simulate large-scale evacuations of such structures. The system is unique because it considers individuals with disabilities explicitly in terms of physical and psychological attributes. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions that have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency. The system was applied to three unique test beds: a multi-story office building, an international airport, and a major sports arena. Several simulation experiments revealed specific areas of concern for both building managers and management practice in general. In particular, we were able to show (a) how long evacuations of heterogeneous populations may be expected to last, (b) who the most vulnerable groups of people are, (c) the risk engendered from particular design features for individuals with disabilities, and (d) the potential benefits from adopting alternate evacuation strategies, among others. Considered together, the findings provide a useful foundation for the development of best practices and policies addressing the evacuation concerns surrounding heterogeneous populations in large, complex environments. Ultimately, a capabilities based approach featuring both tactical and strategic planning with an eye toward the unique problems presented by individuals with disabilities is recommended

Hybrid agent-based and social force simulation for modelling human evacuation egress

Simulation has become one of the popular techniques to model evacuation scenarios. Simulation is used as an instrumental for examining human movement during both normal and emergencies such as evacuation. During an evacuation, people will be in a panic situation and egress behaviour that will find the way out from a dangerous place to a safe place. Two well-known techniques in simulation that can incorporate human behaviour inside the simulation models are Agent-Based Simulation and Social Force Simulation. ABS is using the concept of a multi-agent system that consists of decentralized agents which can be autonomous, responsive and proactive. Meanwhile, SFS is a physical force to drive humans dynamically to perform egress actions and human self-organised behaviour in a group. However, the main issue in modelling both ABS or SFS alone is due to their characteristic as ABS have difficulty in modelling the force element and collective behaviours while SFS does not focus on free movements during the evacuation. This behaviour was due to the structure of humans (agents) inside ABS is decentralized which resulting collision among agents and the desired formation of evacuation was not achieved. On the other hand, in a single SFS model, the human was not proactive in finding the way out which was not reflecting the actual behaviour of humans during the evacuation. Both ABS and SFS are potential techniques to be combined due to their characteristics of self-learning and free movement in ABS and self organization in SFS. The research methodology based on modelling and simulation (M&S) life-cycle has been utilized for this work; consists of three main phases, namely preliminary study, model development and validation and verification and finally the experimentation and the results analysis. The M&S life-cycle was utilized aligned with the research aim which is to investigate the performance of the combined ABS and SFS in modelling the egress behaviour during evacuation. To achieve the aim, five evacuation factors have been chosen namely obstacles, the number of exits, exit width, triggered alarm time, and the number of people that have been the most chosen factors in the literature review. Next, three simulation models (using techniques: SFS, ABS and hybrid ABS/SFS) have been developed, verified, and validated based on the real case study data. Various simulation scenarios that will influence the human evacuation movement based on the evacuation factors were modelled and analysed. The simulation results were compared based on the chosen performance measurement parameters (PMP): evacuation time, velocity, flow rate, density and simulation time (model execution time). The simulation results analysis revealed that SFS, ABS, and hybrid ABS/ SFS were found suitable to model evacuation egress (EE) based on the reported PMP. The smallest standard error (SSE) values reported 66% for hybrid ABS/ SFS, 17% for ABS and 17% for SFS where the highest percentage of SSE indicated the most accurate. Based on the experiment results, the hybrid ABS/ SFS revealed a better performance with high effectiveness and accuracy in the simulation model behaviour when modelling various evacuation egress scenarios compared to single ABS and SFS. Thus, hybrid ABS/ SFS was found the most appropriate technique for modelling EE as agents in the hybrid technique were communicating to each other by forming a decentralised control for smooth and safe EE movement. In addition, the impactful factors that affected the result accuracy were exits, the exit width (size), the obstacle and the number of people. Conclusively, this thesis contributed the hybrid ABS/ SFS model for modelling human behaviour during evacuation in a closed area such as an office building to the body of knowledge. Hence, this research was found significant to assist the practitioners and researchers to study the closer representation of human EE behaviour by considering the hybrid ABS/SFS model and the impactful factors of evacuation

A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment

Understanding pedestrian behaviour in traffic environments is a crucial step in the development and testing of autonomous vehicles. As the environment's most vulnerable road users, pedestrians introduce an element of unpredictability that can lead to dangerous scenarios if their behaviours are unfamiliar to or misinterpreted by vehicles. In this thesis, we present a hierarchical pedestrian behaviour model that interprets high-level decisions through the use of behaviour trees to produce maneuvers that are executed by the low-level motion planner using an adapted Social Force Model. The presented hierarchical model is evaluated on two real-world data sets collected at separate locations with different road structures. The first data set provides a busy four-way intersection with signalized crosswalks, while the second location provides an unsignalized crosswalk across a two-way road at a Canadian university. Our model was shown to replicate the real-world pedestrians' trajectories and decision-making processes with a high degree of accuracy given only high-level routing information (start point, end point, and average walking speed) for each pedestrian. The model is integrated into GeoScenario Server, extending its vehicle simulation capabilities with pedestrian simulation. The extended environment allows simulating test scenarios involving both vehicles and pedestrians to assist in the scenario-based testing process of autonomous vehicles

Integrated design of transport infrastructure and public spaces considering human behavior: A review of state-of-the-art methods and tools

In order to achieve holistic urban plans incorporating transport infrastructure, public space and the behavior of people in these spaces, integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decision-makers. This paper describes a systematic literature review following a four-part framework. Firstly, to understand the relationship of elements of transport, spaces, and humans, we review policy and urban design strategies for promoting positive interactions. Secondly, we present an overview of the integration methods and strategies used in urban design and policy discourses. Afterward, metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed. Finally, this paper gives a review of state-of-the-art tools with a focus on seven computer simulation paradigms. This article explores mechanisms underlying the complex system of transport, spaces, and humans from a multidisciplinary perspective to provide an integrated toolkit for designers, planners, modelers and decision-makers with the current methods and their challenges

Flood-pedestrian simulator: an agent-based modelling framework for urban evacuation planning

Agent-Based Modelling (ABM) is an increasingly used approach for characterisation of human behaviour in evacuation simulation modelling. ABM-based evacuation models used in flood emergency are developed mostly for vehicular scenarios at regional scale. Only a few models exist for simulating evacuations of on-foot pedestrians responding to floods in small and congested urban areas. These models do not include the heterogeneity and variability of individuals’ behaviour influenced by their dynamic interactions with the floodwater properties. This limitation is due to the modelling restrictions pertaining to the computational complexity and the modelling flexibility for agent characterisation. This PhD research has aimed to develop a new ABM-based pedestrian evacuation model that overcomes these challenges through an ABM platform called Flexible Large-scale Agent Modelling Environment for the Graphics Processing Units (FLAME GPU). To achieve this aim, a hydrodynamic model has been integrated into a pedestrian model within the FLAME GPU framework. The dynamic interactions between the flood and pedestrians have been formulated based on a number of behavioural rules driving the mobility states and way-finding decisions of individuals in and around the floodwaters as well as the local changes in the floodwater properties as a result of pedestrians’ crowding. These rules have been progressively improved and their added value has been explored systematically by diagnostically comparing the simulation results obtained from the base setup and the augmented version of the model applied to a synthetic test case. A real-world case study has been further used to specifically evaluate the added value of rules relating the individuals’ way-finding mechanism to various levels of flood-risk perception. The findings from this research have shown that increasing the level of pedestrians’ heterogeneity and the effect of pedestrians’ crowding on the floodwater hydrodynamics yield to a considerably different prediction of flood risk and evacuation time. Besides, accounting for pedestrians’ various levels of flood-risk perception has been found to be one determinant factor in the analysis of flood risk and evacuation time when there are multiple destinations. Finally, the sensitivity analysis on the simulation results have shown that the deviations in the simulation outcomes increases in line with the increase in the sophistication of human behavioural rules

Pilgrim crowd dynamics

Among the steady progression of disasters worldwide lie the numerous instances of fatality where crowds gather. The scale of these is particularly high at the Hajj in Makkah, where there are exceptionally high numbers of pedestrians in a number of confined areas and, depending on the time of year, all in searing heat. In order to reduce the likelihood of repetition in the future, the present thesis involved firstly determining the characteristics of the pedestrians attending the Hajj, and then collecting speed, flow and density data by observing them walking along one of the busiest roads between the Holy Mosque and the other holy sites, Ajyad Street. These were analyzed against various models from the literature including those of Greenshield, Weidmann and Greenberg, and it was found that none of these fitted convincingly, mostly because pilgrims do not walk at the maximum speeds that the crowd density allows. This thesis proposes the use instead of a maximum possible speed model based on a linear relationship between speed and density i.e. $u$ ≤ 1.75 (1 - $k$ /5.47) where $u$ is speed (m/s) and $k$ is density (people/m$^2$). It then goes on demonstrate with a simulation model that an increase of 50% in traffic with the current layout would result in severe overcrowding. This however could be avoided relatively easily by a particular combination of changing the directions of flow and the geometry of the road

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned