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This paper presents a knowledge-based framework for modeling crowd motion in an unfamiliar environment. Three predominant procedures were defined in the framework: knowledge representation, dynamic knowledge transmission, and knowledge-guided wayfinding. First, a semantic-based layered structure is designed to represent knowledge about the unfamiliar environment. Second, to capture important influential factors for knowledge transmission, such as personal influential radius and personal abilities of expressing and assimilating knowledge, we construct a model of personalized knowledge transmission. Third, a probability-based knowledge-guided wayfinding model is presented to produce diverse actions and allow the individuals to adapt to knowledge changes in an unfamiliar environment. Finally, a crowd simulation system is implemented to visualize the analysis in a graphical manner. The proposed method is expected to provide guidance for emergency management especially when the crowd has incomplete knowledge of the environment.

This paper presents an approach for the automatic abstraction of built environments needed for pedestrian dynamics from any building configuration. The approach assesses the usability of navigation mesh to perform realistically pedestrian simulation considering the physical structure and pedestrian abilities for it. Several steps are examined including the creation of a navigation mesh, space subdivision, border extraction, height map identification, stairs classification and parametrisation, as well as pedestrian simulation. A social-force model is utilised to simulate the interactions between pedestrians and an environment. To perform quickly different 2D/3D geometrical queries various spatial indexing techniques are used, allowing fast identification of navigable spaces and proximity checks related to avoidance of people and obstacles in built environments. For example, for a moderate size building having eight floors and a net area of 13,000 m2, it takes only 104 s to extract the required building information to run a simulation. This approach can be used for any building configuration extracting automatically needed features to run pedestrian simulations. In this way, architects, urban planners, fire safety engineers, transport modellers and many other users without the need to manually interact with a building model can perform immediately crowd simulations.

Abstract An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed, and determines the routes in a hierarchical manner. This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results, with visualizations, demonstrate the feasibility of our evacuation route optimization method.

Most of current crowd simulation methods have considered the impact of interindividual emotion on the agent's behavior pattern during emergency evacuations. However, the emotion contagion is not only at the individual level, but also at the contagion in groups. Psychological researches show that the third-party authority also has an impact on emotion contagion. For example, security guards can guide individuals to find exits and calms them; and teachers can lead their students safely evacuate from multi-layer teaching buildings, etc. In this paper, we propose a unified framework to simulate the emergency evacuations in virtual environment. This framework considers four kinds of agents: third-party authority agents, group leaders, members and isolated agents. Firstly, we randomly assign each agent a specific personality and initialize its emotion. Secondly, the emotion contagion in this paper is considered with three aspects: intra-group contagion, inter-group contagion and third-party authority based emotion contagion. We simulate inter-group aggregated behaviors by improving the ASCRIBE and provide a threshold model to simulate inter-group switching behaviors. The third-party authorities exhibit a calm effect on the agents during an evacuation, and we set their negative emotion very low and keep unchanged. Meanwhile, we perform high-level path planning to explore the environments and obtain a cognitive map for navigation purposes. Through quantitative and qualitative experiments, simulation results demonstrate that our proposed model can simulate the emotion contagion in groups during emergency evacuations, and our model outperforms some existing works. [ABSTRACT FROM AUTHOR]

One of the heritages can be denoting to the values of human activity in the past and its cultural oral narratives. To virtualize these heritages, it means to actualize the heritage into the digital content. When attempting to understand a particular cultural heritage site, the challenge here is that the connection to the past is non-existence due to insufficient historical information of the heritage sites. On the other hand, crowd simulation has been widely applied for the purpose of construction and reconstruction of tangible and intangible digital heritage. Therefore, the main objective of this research is to bring past life into digital cultural heritage and it would need the inclusions of the visual information of the surroundings and the people in the past. This paper also investigates the phase-by-phase methodology to deal with crowd simulation of different ethnic groups with heterogeneous behaviors in digital cultural heritage. The crowd is modeled and simulated based on the classical particle-based boid algorithm in virtual heritage environment that includes social behaviors of heterogeneous crowd transpired in an old trading port. With respect to bringing the past life into digital cultural heritage, microscopic based crowd simulation is applied to the complex case such as a multi-ethnic trading port, involving distinguished behavioral patterns through a heterogeneous crowd simulation method. In the simulation, a high-level control method, hierarchical state-machine and group formation model are introduced through inter-ethnic interactions formalism. The results of the assessment and validation have shown that the proposed schemes, models and methods have successfully been deployed in George Town, Malaysia through the proposed methodology. Such a simulation can be beneficial for virtual walkthrough and virtual museum applications. Through several investigations, the advantages of applying this approach in simulating the digital George Town are demonstrated as well as its potential for future developments are identified. [ABSTRACT FROM AUTHOR]

Computer simulations of animal groups are usually performed via individual‐based modelling, where simulated animals are designed on the level of individuals. With this approach, developers are able to capture behavioural nuances of real animals. However, modelling each individual as its own entity has the downside of having a high computational cost, meaning that individual‐based models are usually not suitable for real‐time simulations of very large groups. A common alternative approach is flow‐based modelling, where the dynamics of animal congregations are designed on the group level. This enables researchers to create real‐time simulations of massive phenomena at the cost of biological authenticity. A novel approach called hybrid modelling tries to mix the best of both worlds—precision of individual‐based models and speed of flow‐based ones. An unknown surrounding hybrid model is the question of their biological authenticity and relevance. In this study, we develop a hybrid model for the simulation of herds of grazing sheep. Through Bayesian data analysis, we show that such an approach can encompass several aspects of real‐world sheep behaviour. Our hybrid model is also extremely efficient, capable of simulating herds of more than 1,000 individuals in real time without resorting to graphics processing unit execution. [ABSTRACT FROM AUTHOR]

Computer simulations of crowd behaviors in public emergency have become a hot research field, as they are able to provide rich valuable data for public safety analysis and disaster-preparedness measures. In most of the existing crowd escape simulation systems, human behaviors are often limited to taking flight or running away. However, due to the personality and the current emotion of each individual, they may behave in various ways, such as Samaritan or giving up on life et al. In order to simulate such like diversity crowd behavior, this paper presents an emotion-based diversity behavior model, by adopting the OCEAN personality model and the OCC emotion model, while being enriched with the incorporation of CA-SIRS emotion contagion model. We also consider the effects of Yerkes–Dodson Law on individuals' behaviors, which allows individuals to make authentic behaviors under stressful circumstances. The proposed model can provide more diverse individual behaviors compared to the existing emotion-based escape simulation models. The psychology-based individual modeling enables our model to be applied in various scenarios with slightly parameter adjustments. [ABSTRACT FROM AUTHOR]

Simulation methods that can overcome the limitations of theory and experiment are widely used to predict disaster situations and analyze evacuation safety. To obtain more accurate and realistic simulation results, various situations that arise when people evacuate must be reflected. In this research, regression analysis is applied to existing research on walking speed changes according to crowd density, and we suggest a new equation for walking speed changes of people during evacuation. The suggested equation is applied to crowd evacuation simulations based on the actual viewing angle of a person. In addition, we developed a fallen person algorithm and applied it to examples from the Society of Fire Protection Engineers (SFPE) to analyze the effect a fallen person has on evacuation. Through this research, evacuation safety analysis can be performed for cases involving falling people, and it will be possible to design buildings that can minimize risk and evacuation time in case of an actual emergency situation by analyzing the optimum measures more strictly. [ABSTRACT FROM AUTHOR]

There are many methods of simulating a crowd of people, each with its advantages and disadvantages. The increasing number of risk situations in big cities, such as fires or terrorist attacks, made it important to study the behavior of individuals in this type of situation. The purpose of this work is to develop a mathematical-computational model to simulate crowds in fire situations in order to establish relationships between different variables found in those scenarios. This work uses the instinctive concept of crowds in situations with high levels of stress to configure the interactions between people and the environment in a dynamic multiagent system, where each individual presents different cognitive and motor capacities, so that the safety of a closed environment can be diagnosed quicker than the current methods, as well as establishing relation between the fire stage, number of people and the percentage of survivors of the group, and also evaluate the possibility of applying the agent-environment interactions for the implementation of a function optimization system. [ABSTRACT FROM AUTHOR]

Evacuation plans have been historically used as a safety measure for the construction of buildings. The existing crowd simulators require fully modeled 3D environments and enough time to prepare and simulate scenarios, where the distribution and behavior of the crowd needs to be controlled. In addition, its population, routes or even doors and passages may change, so the 3D model and configurations have to be updated accordingly. This is a time-consuming task that commonly has to be addressed within the crowd simulators. With that in mind, we present a novel approach to estimate the resulting data of a given evacuation scenario without actually simulating it. For such, we divide the environment into smaller modular rooms with different configurations, in a divide-and-conquer fashion. Next, we train an artificial neural network to estimate all required data regarding the evacuation of a single room. After collecting the estimated data from each room, we develop a heuristic capable of aggregating per-room information so the full environment can be properly evaluated. Our method presents an average error of 5 % when compared to evacuation time in a real-life environment. Our crowd estimator approach has several advantages, such as not requiring to model the 3D environment, nor learning how to use and configure a crowd simulator, which means any user can easily use it. Furthermore, the computational time to estimate evacuation data (inference time) is virtually zero, which is much better even when compared to the best-case scenario in a real-time crowd simulator. [ABSTRACT FROM AUTHOR]

Augmenting virtual agents in real crowd videos is an important task for different applications from simulations of social environments to modeling abnormalities in crowd behavior. We propose a framework for this task, namely for augmenting virtual agents in real crowd videos. We utilize pedestrian detection and tracking algorithms to automatically locate the pedestrians in video frames and project them into our simulated environment, where the navigable area of the simulated environment is available as a navigation mesh. We represent the real pedestrians in the video as simple three-dimensional (3D) models in our simulation environment. 3D models representing real agents and the augmented virtual agents are simulated using local path planning coupled with a collision avoidance algorithm. The virtual agents augmented into the real video move plausibly without colliding with static and dynamic obstacles, including other virtual agents and the real pedestrians. [ABSTRACT FROM AUTHOR]

Abstract Despite recent technological advancements in the architecture, engineering, and construction (AEC) industries, the number of fire-caused fatalities in residential and commercial buildings in the United States over the last decade has consistently been an order of magnitude higher than fatalities caused by all other types of natural disasters combined. In this research, EvacuSafe is developed as a tool for designers to allow for layout optimization of buildings and facilities in terms of their evacuation safety performance. Contributions of this research include the development and validation of two spatiotemporal risk indices to quantify the safety of the egress routes and individual building compartments. Other unique advantages of EvacuSafe include the seamless integration of fire dynamics simulation, agent-based crowd simulation and building information models (BIM) using IFC data structures, allowing EvacuSafe to be readily used by designers to analyze a building layout design under various fire scenarios and for layout design optimization based on multiple safety criteria. The development of the framework, the risk indices, and the IFC-based integration is illustrated and validated through a case study on a two-story office building with 59 compartments. The results of the implementation show that the EvacuSafe is a valuable tool for evacuation design and planning that provides a more comprehensive evaluation of the evacuation performance in comparison to the existing indices and safety measures in the industry. Highlights • Two spatiotemporal risk indices, route risk index (RRI) and compartment risk index (CRI), are introduced. • EvacuSafe framework integrates BIM, ABS and fire dynamics simulation to evaluate the safety of evacuation. • Industry Foundation Classes (IFC) data structure is applied to build a seamless data transfer between different modules. • EvacuSafe is implemented on a two-story building to demonstrate its capabilities in evaluation of evacuation safety process. [ABSTRACT FROM AUTHOR]

Abstract We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework to generate crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior (personality), flow, lighting conditions, viewpoint, type of noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by augmenting a real dataset with it and improving the accuracy in pedestrian detection and crowd classification. Furthermore, we evaluate the impact of removing the variety in different LCrowdV parameters to show the importance of the diversity of data generated from our framework. LCrowdV has been made available as an online resource. [ABSTRACT FROM AUTHOR]

These instructions give you guidelines for preparing papers for IEEE Transactions and Journals. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd-Fe-B"). Do not write "(Invited)" in the title. Full names of authors are preferred in the author field, but are not required. Put a space between authors' initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstract must be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must be between 150-250 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstract must be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct. [ABSTRACT FROM AUTHOR]

Agent-based crowd simulations are used for modelling building and space usage, allowing designers to explore hypothetical real-world scenarios, including extraordinary events such as evacuations. Existing work which engages virtual reality (VR) as a platform for crowd simulations has been primarily focussed on the validation of simulation models through observation; the use of interactions such as gaze to enhance a sense of immersion; or studies of proxemics. In this work, we extend previous studies of proxemics and examine the effects of varying crowd density on user experience and behaviour. We have created a simulation in which participants walk freely and perform a routine manual task, whilst interacting with agents controlled by a typical social force simulation model. We examine and report the effects of crowd density on both affective state and behaviour. Our results show a significant increase in negative affect with density, measured using a self-report scale. We further show significant differences in some aspects of user behaviours, using video analysis, and discuss how our results relate to VR simulation design for mixed human-agent scenarios. [ABSTRACT FROM AUTHOR]

Existing crowd evacuation guidance systems require the manual design of models and input parameters, incurring a significant workload and a potential for errors. This paper proposed an end-to-end intelligent evacuation guidance method based on deep reinforcement learning, and designed an interactive simulation environment based on the social force model. The agent could automatically learn a scene model and path planning strategy with only scene images as input, and directly output dynamic signage information. Aiming to solve the “dimension disaster” phenomenon of the deep Q network (DQN) algorithm in crowd evacuation, this paper proposed a combined action-space DQN (CA-DQN) algorithm that grouped Q network output layer nodes according to action dimensions, which significantly reduced the network complexity and improved system practicality in complex scenes. In this paper, the evacuation guidance system is defined as a reinforcement learning agent and implemented by the CA-DQN method, which provides a novel approach for the evacuation guidance problem. The experiments demonstrate that the proposed method is superior to the static guidance method, and on par with the manually designed model method.