To minimize property loss and death count in terror attacks and other emergent scenarios, attention given to timely and effective evacuation cannot be enough. Due to limited evacuation resource, i.e., number of available exits, there exists interdependence among pedestrians such as cooperation, competition and herd effect. Thus human factors - more specifically, pedestrians' behavior in emergency evacuation - play a significant role in evacuation research. Effective evacuation can only be reached when route planning are considered in conjunction with psychological dynamics, which is often ignored. As another drawback, previous research assumes the environment including available exits as stationary. However, we note that during emergency, some exits which are not often utilized in normal times are opened, which potentially helps if pedestrians are aware of them. In this paper, we analyze the effect of pedestrians' behavior, i.e., herd effect and knowledge of changing environment with Cellular Automata (CA) simulation. Results of the simulation show the harmful effect of herd effect as well as highlight the importance of timely informing pedestrians of environmental change. Accordingly, we propose policy and procedural recommendations for emergency management of large, crowded structures. Our future work includes considering more human factors and applying our model to log data provided by videos in public venues, which can further show effectiveness of our model in real scenarios.
@cite_12 the likely behaviours of train passengers in an emergency evacuation are explored in the form of questionnaires. The results showed that respondents are more likely to be reactive (e.g., wait for instruction from station staff) rather than proactive (e.g., move to exit); more likely to be cooperative (e.g., helping other people) than competitive (e.g., push other passengers); more likely to show herding or symmetry behaviour (e.g., following other passengers) than symmetric breaking behaviour (e.g., choose least crowded exit); and less likely to use the lifts, escalators and tunnels to escape in an emergency situation. In terms of demographic differences in behaviours, it's demonstrated that there are significant differences in the evacuation behaviours between males and females. Males are less likely to use emergency call buttons, call the emergency phone number or wait at the assembly area. Also, they are more likely to display competitive behaviour, choose the least crowded exit, and use the lifts, escalators and tunnels to escape in the event of an emergency evacuation. In contrast, differences in behaviours are not as obvious among different age groups.
@cite_12
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Abstract This paper explores the likely behaviours of train passengers in an emergency evacuation and examines four crucial theoretical issues on the passengers’ evacuation, including reactive vs. proactive behaviours, cooperative vs. competitive behaviours, symmetry breaking, and route exit choice. A survey of 1134 train passengers shows that respondents are not homogeneous in their likely behaviours. Overall, they are more likely to be reactive (e.g., wait for instruction from station staff) than proactive (e.g., move to exit) in an emergency situation. We also find that people are more likely to be co-operative (e.g., helping other people) than competitive (e.g., push other passengers). Although passengers are likely to show herding or symmetry breaking behaviour (e.g., following other passengers) than symmetric behaviour (e.g., choose least crowded exit), the degree of symmetry breaking behaviour is not as high as assumed in previous mathematical models. They are also unlikely to use escalators, lifts and train tunnels in their exit route choice during an emergency escape. In terms of demographic differences in behaviours, results from the ordered logit models demonstrate that there are significant differences in the evacuation behaviours between males and females but not among the different age groups. Besides providing valuable information for developing mathematical models intended to simulate passengers’ evacuation in a train station, our findings can assist managers of emergency response in developing appropriate strategies and training, and in designing solutions and education campaigns for effective evacuation. Emergency evacuation from airports is an important consideration given the continuing occurrence of both natural and human caused disasters. Unfortunately, the traditional evacuation-drill approach to prepare for emergency situations presents several practical challenges at the scale and magnitude required for airports. In this paper, we present an agent-based model (ABM) called exitus which is capable of determining the extent to which collective behavior and overall evacuation time of passenger groups is affected by changes in the built environment for large, complex structures. The model is unique because it explicitly considers the physical and psychological characteristics of individuals with disabilities. In our first experiment, several bomb simulation scenarios were conducted at an international airport using exitus. Several important findings were revealed including: 1) the importance of stairway and exit configurations; 2) the inherent weaknesses of the pier airport design in affecting timely evacuations; 3) who the most vulnerable groups of people are; 4) the particular risk engendered from crowded or complex building interiors for individuals with disabilities; and 5) the potential problems caused by locating explosive detection system machines near passenger processing areas. The results of a second exploratory experiment also revealed the importance of realistically modeling psychological attributes of individuals with disabilities and their potential impact on collective evacuation performance. Overall the findings demonstrated the model’s ability to generate a common operational picture capable of guiding preparedness efforts for both public and private organizations encompassing a wide variety of professional endeavor. Pedestrian evacuation is one open question in a myriad of scientific disciplines and has attracted considerable attention during the past decades. Aim to issue, various research approaches, such as mathematical modeling and simulation, experimental studied and socio-psychology surveys, have been extensively employed. Different from previous frameworks, here a behavior-based cellular automaton model is proposed, which involves the environmental characteristics and neighbors' behaviors. Given the certain range of degree of emergency, it is shown that enhancing the degree of emergency will shorten evacuation time yet decrease cooperation enthusiasm. The larger the degree of familiarity is, the shorter the evacuation time will be. In variance, larger the dependence of pedestrians will prolong the evacuation time. Besides, the novel model also produces some interesting self-organization phenomena, such as the collective behavior at the beginning of the evacuation and arch-like blocking at the end of the evacuation. When our model is finally applied to the evacuation scenario where a room has two symmetrically located exits, the symmetry breaking effect takes place. Our model may shed new light to the study of pedestrian dynamics in realistic world. This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework. Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN)-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES) multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process. In this paper we present a model based on the parallel computational tool of cellular automata CA capable of simulating the process of disembarking in a small airplane seat layout, corresponding to Airbus A320 Boeing 737 layout, in search of ways to make it faster and safer under normal evacuation conditions, as well as emergency scenarios. The proposed model is highly customizable, with the number of exits, the walking speed of passengers, depending on their sex, age and height, and the effects of retrieving and carrying luggage. Additionally, the presence of obstacles in the aisles as well as the emergence of panic being parameters whose values can be varied in order to enlighten the disembarking and emergency evacuation processes are considered in detail. The simulation results were compared to existing aircraft disembarking and evacuation times and indicate the efficacy of the proposed model in investigating and revealing passenger attributes during these processes in all the examined cases. Moreover, we parallelized our code in order to run on a graphics processing unit GPU using the CUDA programming language, speeding up the simulation process. Finally, in order to present a fully dynamical anticipative real-time system helpful for decision-making we implemented the proposed CA model in a field programmable gate array FPGA device, and recreated the results given by the software simulations in a fraction of the time. We then compared and exported the performance results among a sequential software implementation, the implementation running on a GPU, and a hardware implementation, proving the consequent acceleration that results from the parallel CA implementation in specific hardware. Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed @math . The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [CGGKMP14] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most @math and also proved that any algorithm has evacuation time at least @math . We improve both the upper and lower bounds on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most @math and show that any algorithm has evacuation time at least @math . To achieve the upper bound, we designed an algorithm which non-intuitively proposes a forced meeting between the two robots, even if the exit has not been found by either of them. Assume that two robots are located at the centre of a unit disk. Their goal is to evacuate from the disk through an exit at an unknown location on the boundary of the disk. At any time the robots can move anywhere they choose on the disk, independently of each other, with maximum speed @math . The robots can cooperate by exchanging information whenever they meet. We study algorithms for the two robots to minimize the evacuation time: the time when both robots reach the exit. In [9] the authors gave an algorithm defining trajectories for the two robots yielding evacuation time at most @math and also proved that any algorithm has evacuation time at least @math . We improve both the upper and lower bounds on the evacuation time of a unit disk. Namely, we present a new non-trivial algorithm whose evacuation time is at most @math and show that any algorithm has evacuation time at least @math . To achieve the upper bound, we designed an algorithm which non-intuitively proposes a forced meeting between the two robots, even if the exit has not been found by either of them. This paper offers a descriptive account of Twitter (a microblogging service) across four high-profile, mass convergence events - two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behaviour is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology. Starting with a short review of the available literature in the field of pedestrian and evacuation research, an overview is given over the observed collective phenomena in pedestrian crowds. This includes lane formation in corridors and oscillations at bottlenecks in normal situations, while different kinds of blocked states are produced in panic situations. By means of molecular-dynamic-like microsimulations based on a generalized force model of interactive pedestrian dynamics, the spatio-temporal patterns in pedestrian crowds are successfully reproduced and interpreted as self-organized phenomena. In contrast to previous socio-psychological approaches, this allows a physical understanding of the observations. Despite the significantly different phenomena occuring in normal and panic situations, the main effects can be described by a unified model containing only well interpretable and plausible terms. The transition between the “rational” normal behavior and the apparently “irrational” panic behavior is controlled by a single parameter, the “nervousness”, which influences fluctuation strengths, desired speeds, and the tendency of herding. Thereby, it causes paradoxial effects like “freezing by heating”, “faster is slower”, and the ignorance of available exits. Nevertheless, there are measures to improve pedestrian flows, both in normal and panic situations. For example, the suitable placement of columns can help, although they reduce the accessible space. Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology. Opportunistic communications (oppcomms) use low-cost human wearable mobile nodes allowing the exchange of packets at a close range of a few to some tens of meters with limited or no infrastructure. Typically cheap pocket devices which are IEEE 802.15.4-2006 compliant can be used and they can communicate at 2m to 10m range, with local computational capabilities and some local memory. In this paper we consider the application of such devices to emergency situations when other means of communication have broken down. This paper evaluates whether oppcomms can improve the outcome of emergency evacuation in directing civilians safely. We describe an autonomous emergency support system (ESS) based on oppcomms to support evacuation of civilians in a built environment such as a building or supermarket. The proposed system uses a fixed infrastructure of sensor nodes (SNs) to monitor the environment. Hazard information obtained via SNs is disseminated to the individuals, and they spread among the people who are located in this built environment using oppcomm devices carried by these people. The information received by these people can then guide them safely to the exits as the emergency situation evolves over time. We evaluate our scheme using a distributed multi-agent building evacuation simulator (DBES) in the context of evacuation scenarios of a multi-storey office building in the presence of a fire that is spreading. The results show the degree of improvement that the oppcomms can offer. c � 2011 Published by Elsevier Ltd. A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, more modular network architectures that exploit emergent task decomposition strategies can evolve faster and outperform comparably smaller non emergent neural networks for this task. Much like biological nervous systems, larger Emergent Task Decomposition Networks appear to evolve faster than comparable smaller networks. Unlike reinforcement learning techniques, only a global fitness function is specified, requiring limited supervision, and self-organized task decomposition is achieved through competition and specialization. The results are derived from computer simulations. Emergency applications have recently become widely available on modern smart phones. Nearly all of these commercial applications have focused on providing simple accident information in outdoor settings. AR in indoor environments poses unique challenges, due to the unavailability of GPS indoors and WiFi-based positioning limitations. In this paper, we propose the use of Rescue Me, a novel system based on indoor mobile AR applications using personalized pedometry and one that recommends the most optimal, uncrowded exit path to users. We have developed the Rescue Me application for use within large scale buildings, with complex paths. We show how Rescue Meleverages the sensors on a smart phone, in conjunction with emergency information and daily-based user behavior, to deliver evacuation information in emergency situations. In an emergency, wireless network sensors combined with a navigation algorithm could help safely guide people to a building exit while helping them avoid hazardous areas. We propose a distributed navigation algorithm for emergency situations. At normal time, sensors monitor the environment. When the sensors detect emergency events, our protocol quickly separates hazardous areas from safe areas, and the sensors establish escape paths. Simulation and implementation results show that our scheme achieves navigation safety and quick convergence of the navigation directions. We based our protocol on the temporally ordered routing algorithm for mobile ad hoc networks. TORA assigns mobile nodes temporally ordered sequence numbers to support multipath routing from a source to a specific destination node It is a characteristic of swarm robotics that specifying overall emergent swarm behaviours in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation we need such specifications. This paper explores the use of temporal logic to formally specify, and possibly also prove, the emergent behaviours of a robotic swarm. The paper makes use of a simplified wireless connected swarm as a case study with which to illustrate the approach. Such a formal approach could be an important step toward a disciplined design methodology for swarm robotics. We examine the meaning of causality in calculi for mobile processes like the π-calculus, and we investigate the relationship between interleaving and causal semantics for such calculi. We separate two forms of causal dependencies on actions of π-calculus processes, called subject and object dependencies: The former originate from the nesting of prefixes and are propagated through interactions among processes (they are the only form of causal dependencies present in CCS-like languages); the latter originate from the binding mechanisms on names. We propose a notion of causal bisimulation which distinguishes processes which differ for the subject or for the object dependencies. We show that this causal equivalence can be reconducted to, or implemented into, the ordinary interleaving observation equivalence. We prove that our encoding is fully abstract w.r.t. the two behavioural equivalences. This allows us to exploit the simpler theory of the interleaving semantics to reason about the causal one. In [San94b] a similar programme is carried out for location bisimulation [BCHK91], a non-interleaving spatial-sensitive (as opposed to causal-sensitive) behavioural equivalence. The comparison between the encodings of causal bisimulation in this paper, and of location bisimulation in [San94b], evidences the similarities and the differences between these two equivalences. Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning3–5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space7,8 and is critical for integrating self-motion (path integration)6,7,9 and planning direct trajectories to goals (vector-based navigation)7,10,11. Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation7,10,11, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments. Multi-site phosphorylation is ubiquitous in cell biology and has been widely studied experimentally and theoretically. The underlying chemical modification mechanisms are typically assumed to be distributive or processive. In this paper, we study the behaviour of mixed mechanisms that can arise either because phosphorylation and dephosphorylation involve different mechanisms or because phosphorylation and or dephosphorylation can occur through a combination of mechanisms. We examine a hierarchy of models to assess chemical information processing through different mixed mechanisms, using simulations, bifurcation analysis and analytical work. We demonstrate how mixed mechanisms can show important and unintuitive differences from pure distributive and processive mechanisms, in some cases resulting in monostable behaviour with simple dose–response behaviour, while in other cases generating new behaviour-like oscillations. Our results also suggest patterns of information processing that are relevant as the number of modification sites increases. Overall, our work creates a framework to examine information processing arising from complexities of multi-site modification mechanisms and their impact on signal transduction. The emergence and actions of the so-called Islamic State of Iraq and the Levant (ISIL ISIS) has received widespread news coverage across the World, largely due to their capture of large swathes of land across Syria and Iraq, and the publishing of execution and propaganda videos. Enticed by such material published on social media and attracted to the cause of ISIS, there have been numerous reports of individuals from European countries (the United Kingdom and France in particular) moving to Syria and joining ISIS. In this paper our aim to understand what happens to Europe-based Twitter users before, during, and after they exhibit pro-ISIS behaviour (i.e. using pro-ISIS terms, sharing content from pro-ISIS accounts), characterising such behaviour as radicalisation signals. We adopt a data-mining oriented approach to computationally determine time points of activation (i.e. when users begin to adopt pro-ISIS behaviour), characterise divergent behaviour (both lexically and socially), and quantify influence dynamics as pro-ISIS terms are adopted. Our findings show that: (i) of 154K users examined only 727 exhibited signs of pro-ISIS behaviour and the vast majority of those 727 users became with such behaviour during the summer of 2014 when ISIS shared many beheading videos online; (ii) users exhibit significant behaviour divergence around the time of their activation, and; (iii) social homophily has a strong bearing on the diffusion process of pro-ISIS terms through Twitter.