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In recent years, the field of crowd simulation has experienced significant advancements, attributed in part to the improvement of hardware performance, coupled with a notable emphasis on agent-based characteristics. Agent-based simulations stand out as the preferred methodology when researchers seek to model agents with unique behavioral traits and purpose-driven actions, a crucial aspect for simulating diverse and realistic crowd movements. This survey adopts a systematic approach, meticulously delving into the array of factors vital for simulating a heterogeneous microscopic crowd. The emphasis is placed on scrutinizing low-level behavioral details and individual features of virtual agents to capture a nuanced understanding of their interactions. The survey is based on studies published in reputable peer-reviewed journals and conferences. The primary aim of this survey is to present the diverse advancements in the realm of agent-based crowd simulations, with a specific emphasis on the various aspects of agent behavior that researchers take into account when developing crowd simulation models. Additionally, the survey suggests future research directions with the objective of developing new applications that focus on achieving more realistic and efficient crowd simulations. [ABSTRACT FROM AUTHOR]

Crowd simulation has been widely used in evacuation exercises, games or movie manufacturing, and many other fields. How to plan reasonable trajectories for pedestrians in a scene is always one of the critical problems in crowd simulation. Traditional simulation methods have the problem of large differences between simulated and actual trajectories, and it is difficult to generate near-real and reasonable multimodal pedestrian trajectories. In this paper, we propose a novel method utilizing generative models for crowd simulation: GAN with Incubator and Extender (IE-GAN). This data-driven model learns the movement laws of pedestrians from real datasets, and simulates a full movement trajectory for the "dummy" without corresponding situations in the dataset through a unique model architecture. In our method, the generated initial trajectory and further trajectories constitute the full trajectory of the "dummy". Incubator networks based on long-term memory network (LSTM) are used to generate the initial trajectory, and the further trajectory is generated by the Extender, which is based on a generative adversarial network (GAN). The experimental results show that the trajectories generated by our model can approach real human's trajectories. [ABSTRACT FROM AUTHOR]

The deployment of security guards in large public spaces is a promising research topic with a wide range of applications. Existing methods are mainly based on manual design approaches, which are neither effective nor flexible enough for large-scale scenarios. To address this issue, this paper proposes an evolutionary framework to automatically generate the optimal deployment strategy of security guards in large public spaces. The proposed method includes a new metric for automatically evaluating deployment strategies, as well as an evolutionary solver based on differential evolution to optimize the deployment strategy automatically. To evaluate its effectiveness, the proposed evolutionary framework is tested on two synthetic scenarios with different characteristics and one real-world scenario. The results demonstrate that the proposed framework outperforms several commonly used strategies in terms of the response time of security guards. [ABSTRACT FROM AUTHOR]

Formation maintenance and collision avoidance allow multiple agents to form and maintain a specific formation in the environment and reach the final goal safely without any collisions, which are important component of the multi-agent/crowd cooperation field. We propose a two-level formation control method to simulate crowd formation. At the low level, a comprehensive group reward function is designed to train the agents to learn to collaborate to reach the goal and avoid collisions with other agents and obstacles. The Multi-Agent POsthumous Credit Assignment algorithm and curriculum learning are combined to solve the credit assignment problem in the shared group reward and the slow convergence problem of the policy in the reward-sparse environment. At the high level, we design a formation switch method to determine the compression or splitting of the formation for multiple agents passing through the narrow exit. Then the optimal reciprocal collision avoidance algorithm is adopted to compute a collision-free velocity for the team of multiple agents, on the basis of which the desired formation positions are calculated to avoid collisions between the team and the obstacles in the environment. Lastly, Hungarian algorithm is utilized to assign the formation positions to the agents as their respective target position to accelerate the process of forming. Experimental results demonstrate that our proposed approach enable multiple agents to constitute the formation shape from the starting positions quickly and maintain the shape well when moving toward the final goal. Furthermore, our model has a certain generalization ability to unseen formation shapes in the scenarios without and with obstacles. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the past, evolution, and new perspectives in crowd simulation. Many work have been produced and published in this area that was launched approximately 30 years ago. In this paper, we re-visited the main aspects of the area, presenting the periods and evolution we had in the past. In addition, we also discuss the present and possible trends for the future. [ABSTRACT FROM AUTHOR]

Social force model is one of the well-known approaches that can successfully simulate pedestrians' movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population. [ABSTRACT FROM AUTHOR]

Traditional methods for constructing crowd simulations often have shortcomings in terms of realism, and data-driven methods are an effective approach to enhancing the visual realism of crowd simulation. However, existing work mainly constructs crowd simulations through prediction-based approaches based on deep learning or by fitting the parameters of traditional methods, which limits the expressiveness of the model. In response to these limitations, this paper introduces a method capable of generating realistic pedestrian crowds. This approach uses a Generative Adversarial Network, complemented by a transformer module, to learn behavioral patterns from actual crowd trajectories. We use a transformer module to extract trajectory features of the crowd, then convert the spatial relationships between individuals into sequences using a special data processing mechanism, and use the transformer module to extract social features of the crowd, while guiding the movement of each individual with their target direction. During training, we simultaneously learn from real crowd data and simulation data resolving collisions by traditional methods, to enhance the collision avoidance behavior of virtual crowds while maintaining the movement patterns of real crowds, resulting in more general collision avoidance behavior. The crowds generated by the model are not limited to specific scenarios and show generalization capabilities. Compared to other models, our method shows better performance on publicly available large-scale pedestrian datasets after training. Our code is publicly available at https://github.com/ydp91/NPCGAN. [ABSTRACT FROM AUTHOR]

Multi-agent simulations can provide useful insights into the movement of pedestrians in arbitrary environments for predictive planning and analysis. The fidelity of such agents is important for the validity of the associated analyses. Current methods tend to employ agent models that are largely homogeneous in both physical abilities and behaviours. However, actual pedestrians exhibit a wide range of locomotion abilities and behaviours. In this work, we take a first step towards identifying and modelling distracted behaviours, such as walking and texting on a cell phone. Our models relate reported changes to the locomotion patterns and sensory abilities of distracted pedestrians to the corresponding parameters of a commonly used crowd simulation steering approach. We demonstrate experimentally that accounting for even a few of these behaviours significantly alters the flow patterns of the simulated agents. This impact affects overall crowd behaviour and is reflected in several crowd statistics including flow rate, effort, and kinetic energy. [ABSTRACT FROM AUTHOR]

In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm for generating tens of thousands of high-level agent paths from this specification. This algorithm allows our specification to be used with traditional crowd simulation obstacle avoidance algorithms while still maintaining the realism required for the complex, real-world simulations of a transit facility. Our evaluation with industry professionals shows that our approach is intuitive and provides controls at the right level of detail to be used in large facilities (200,000+ people/day). [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]

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]

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]

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]

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]

Crowd simulations span a wide spectrum of application domains, most notably video games, evacuation scenarios, and the movie industry. However, it is not obligatory that all virtual populace applications have the primary objective of realistic simulation. In most instances, it is necessary and sufficient that viewers perceive the crowd as plausible. Even for a crowd consisting of agents navigating on linear trajectories without any maneuvers, visual motion illusion elicited by these trajectories might appear to be a natural consequence, causing them to be perceived as wriggling rather than straight. In this respect, we evaluate in this study whether simulated 3D human agents walking with constant, collision-free velocities, induce such a maneuvering motion illusion, aiming toward an efficient real-time crowd simulation. For this purpose, we recorded videos of virtual human crowds with different parameter combinations, such as the agent walking speed, crowd density, camera tilt angle, and camera distance. These videos were watched by human subjects who were instructed to mark the virtual agents who they thought had changed their gait directions. The analyzed results revealed that participants claimed the presence of maneuvering virtual agents in the videos, even though there were none in any of them. Spatial grouping of the markings highlighted that the participants mainly focused on the central area of the simulation environment, and spatiotemporal analysis of the click data also showed stronger evidence to such an illusion (see accompanying video). Furthermore, we found that all of the referred parameters have statistically significant main effects on the number of marked agents per watched video. [ABSTRACT FROM AUTHOR]

Pedestrian behavior is an omnipresent topic, but the underlying cognitive processes and the various influences on movement behavior are still not fully understood. Nonetheless, computational simulations that predict crowd behavior are essential for safety, economics, and transport. Contemporary approaches of pedestrian behavior modeling focus strongly on the movement aspects and seldom address the rich body of research from cognitive science. Similarly, general purpose cognitive architectures are not suitable for agents that can move in spatial domains because they do not consider the profound findings of pedestrian dynamics research. Thus, multi-agent simulations of crowd behavior that strongly incorporate both research domains have not yet been fully realized. Here, we propose the cognitive agent framework Spice. The framework provides an approach to structure pedestrian agent models by integrating concepts of pedestrian dynamics and cognition. Further, we provide a model that implements the framework. The model solves spatial sequential choice problems in sufficient detail, including movement and cognition aspects. We apply the model in a computer simulation and validate the Spice approach by means of data from an uncontrolled field study. The Spice framework is an important starting point for further research, as we believe that fostering interdisciplinary modeling approaches will be highly beneficial to the field of pedestrian dynamics. [ABSTRACT FROM AUTHOR]

Behavioral plausibility is one of the major aims of crowd simulation research. We present a novel approach that simulates communication between the agents and assess its influence on overall crowd behavior. Our formulation uses a communication model that tends to simulate human-like communication capability. The underlying formulation is based on a message structure that corresponds to a simplified version of Foundation for Intelligent Physical Agents Agent Communication Language Message Structure Specification. Our algorithm distinguishes between low- and high-level communication tasks so that ACMICS can be easily extended and employed in new simulation scenarios. We highlight the performance of our communication model on different crowd simulation scenarios. We also extend our approach to model evacuation behavior in unknown environments. Overall, our communication model has a small runtime overhead and can be used for interactive simulation with tens or hundreds of agents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Generating large human crowds of distinguishable individuals is one of the challenges in the gaming industry. When the scene contains many characters, it becomes impracticable to create all the individual characters manually. However, the requirement for the different appearances of individuals in a crowd, namely their faces, is now in greater demand. Therefore, this paper describes our solution to the automatic generation of human faces that are created as a composite of facial parts of 3D scans of real human faces. However, the user has the possibility to further adjust the composite by designing replacements, leading to a desired appearance. The final composite can be exported and attached to a given avatar. To evaluate the usability of our solution, we performed two case studies. The conducted perception study performed with 104 participants aimed to confirm the decreasing human ability to recognize morphologically modified faces. The morphological study focused on the quantification of the extent of facial modifications. Both studies were performed by domain experts from psychology and anthropology. [ABSTRACT FROM AUTHOR]

Simulation of crowds demands coping with scalability and performance issues that are not usually well supported by general purpose agent based simulation toolkits. On the other side, the use of agent models provides a great degree of flexibility in the specification of the behaviour of the entities and their interactions. The agent architecture that is presented in this work addresses both types of requirements, by taking advantage of the characteristics of its specific problem domain: the simulation of crowds in indoor environments. Several algorithms are implemented to improve the efficiency of the management of a high number of agents in order to cope with the performance in the processing of their movements and their representation. At the same time, different models are supported to specify decision making of the agents in order to allow rich behaviours. Agents can represent different types of entities such as people, sensors and actuators. This is illustrated with a realistic case study of the evacuation of the building of our Faculty of Computer Science, where different types of human behaviours are modelled in this kind of situations. [ABSTRACT FROM AUTHOR]

In fires, people are easier to lose their mind. Panic will lead to irrational behavior and irreparable tragedy. It has great practical significance to make contingency plans for crowd evacuation in fires. However, existing studies about crowd simulation always paid much attention on the crowd density, but little attention on emotional contagion that may cause a panic. Based on settings about information space and information sharing, this paper proposes an emotional contagion model for crowd in panic situations. With the proposed model, a behavior mechanism is constructed for agents in the crowd and a prototype of system is developed for crowd simulation. Experiments are carried out to verify the proposed model. The results showed that the spread of panic not only related to the crowd density and the individual comfort level, but also related to people's prior knowledge of fire evacuation. The model provides a new way for safety education and evacuation management. It is possible to avoid and reduce unsafe factors in the crowd with the lowest cost. [ABSTRACT FROM AUTHOR]

Realistic crowd simulation is an important issue for the production of virtual worlds for games, crowd management, public space design, education, entertainment or architectural and urban planning. In this paper, crowd simulation is considered from two aspects: intra-group simulation and inter-group simulation. We propose a unified framework for crowd simulation in real-time virtual environment. Based on this framework, for intra-group simulation, we propose a novel density-based information crowd simulation to collision-free. For inter-group simulation, we propose a novel discrete choice (DC) model to realistic simulation of crowds and path planning. Meanwhile, we also propose a variable bounding box method for intra-group/inter-groups intersection problem. The simulation results show that the developed framework allows different group structures to be easily modeled. And the proposed framework could be used for real-time navigation of many moving crowd in complicated virtual environments. [ABSTRACT FROM AUTHOR]

Most prior crowd simulations do not have groups of people moving in a social manner. In our work, we use a two-level steering system based on two classes: group agent and pedestrian agent. By interpolating the current and desired slot positions of the group agent according to formation templates, dynamic social group formations can be achieved and can also adapt to the width of passageways using our robust and optimized ray casting technique. Based on this interpolation approach, slot-locking keeps subgroups in a group shoulder-to-shoulder regardless of the current formation assuming sufficient surrounding space exists. At times pal social gestures between adjacent members may occur. We also introduce the social FIFO queue to be used in situations such as waiting for the bus. In the subway scene, we describe a seating strategy for passengers entering the subway car and being aware of your presence. In an immersive evaluation using an Oculus DK2 head-mounted display, participants validated the realism of dynamic social group behavior for navigation and making use of public transportation. [ABSTRACT FROM AUTHOR]

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments. [ABSTRACT FROM AUTHOR]

Studies of past emergency events have revealed that occupants' behaviors, egress signage system, local geometry, and environmental constraints affect crowd movement and govern the building evacuation. In addition to complying with code and standards, building designers need to consider the occupants' social characteristics and the unique layout of the buildings to design occupant-centric egress systems. This paper describes an agent-based egress simulation tool, SAFEgress, which incorporates important human and social behaviors observed by researchers in safety and disaster management. Agents in SAFEgress are capable of perceiving building emergency features in the virtual environment and deciding their behaviors and navigation. In particular, we describe four agent behavioral models, namely following familiar exits, following cues from building features, navigating with social groups, and following crowds. We use SAFEgress to study how agents (mimicking building occupants) react to different signage arrangements in a modeled environment. We explore agents' reactions to cues as an emergent phenomenon, shaped by the interactions among groups and crowds. Simulation results from the prototype reveal that different designs of building emergency features and levels of group interactions can trigger different crowd flow patterns and affect overall egress performance. By considering the occupants' perception about the emergency features using the SAFEgress prototype, engineers, designers, and facility managers can study the human factors that may influence an egress situation and, thereby, improve the design of SAFEgress systems and procedures. [ABSTRACT FROM AUTHOR]

Crowd simulations are mostly employed to compose a background in the current scene. For such ambient crowds, it might be unnecessary to perform complex steering calculations. In this study, a steering-free crowd simulation which eliminates the computational cost arising from expensive steering maneuvers is presented. For this purpose, agents are assigned a linear trajectory that is guaranteed to be collision free before entering the simulation. These trajectories are calculated using readily available rendering pipeline of the GPU. To that end, existing agents' bounding discs are rendered in a spatio-temporal manner as each one forms a straight 3D tube and a projection from a selected initial position is captured. Using the blank areas (holes) in this image, it is possible to determine a suitable constant velocity (a goal position and a speed). In experiments, we not only assess three different methods to choose one of the candidate solutions, but also compare our approach with an existing work. Test results reveal that our technique gives better results in both populating an empty environment with agents quicker and reaching a higher maximum number of agents than the existing method. [ABSTRACT FROM AUTHOR]

The quality of a crowd simulation model is determined by its agents' local and global trajectory efficiency. While an agent-based model can accurately handle the local trajectories, global decisions usually are handled by a global path planner. However, most of the global path planning techniques do not consider other agents and their possible paths and the future global flow in the environment. In this paper, we propose a composite system that takes future agent configurations into account via a modified A* algorithm to create a global path plan and combines the global path plan with a local navigation model. We show that the agents using the proposed model intelligently plan their paths based on the dynamic configuration of the environment. In order to balance the performance vs. trajectory quality trade-off, we propose a hierarchical grid structure and discuss its effects on both trajectory quality and computational performance. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel framework to simulate the crowd behavior under emergency situations in a confined space with multiple exits. In our work, we take the information asymmetry into consideration, which is used to model the different behaviors presented by pedestrians because of their different knowledge about the environment. We categorize the factors influencing the preferred velocity into two groups, the intrinsic and extrinsic factors, which are unified into a single space called influence space. At the same time, a finite state machine is employed to control the individual behavior. Different strategies are used to compute the preferred velocity in different states, so that our framework can produce the phenomena of decision change. Our experimental results prove that our framework can be employed to analyze the factors influencing the escape time, such as the number and location of exits, the density distribution of the crowd and so on. Thus it can be used to design and evaluate the evacuation plans. [ABSTRACT FROM AUTHOR]

This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation, pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field. [ABSTRACT FROM AUTHOR]

Crowd simulation is typically an expensive task. We introduce a level of detail system, useful for varied animated crowds, capable of handling several thousands of different animated characters at interactive frame rates. This is accomplished using two complementary structures to reduce memory consumption and optimize the rendering stage. The first structure is a skeleton, with associated octrees per limb, that is used for computing level of detail of geometry and animation. The second structure is a tiling of the scene that is used to select the character's level of detail for geometry, animation and behavior. A quadtree is built on top of this tiling and used for further rendering optimization, allowing us to combine geometry from different characters in parts of the scene that are far away from the camera. The system outperforms similar methods in memory requirements and/or complexity and is capable of rendering crowds composed of a quarter million characters. [ABSTRACT FROM AUTHOR]

Finding optimal trajectories for non-accelerating, non-holonomic agents is a well-understood problem. However, in video games, robotics, and crowd simulations non-holonomic agents start and stop frequently. With the vision of improving crowd simulation, we find optimal paths for virtual agents accelerating from a standstill. These paths are designed for the 'ideal', initial stage of planning when obstacles are ignored. We analytically derive paths and arrival times using arbitrary acceleration angle thresholds. We use these paths and arrival times to find an agent's optimal ideal path. We then numerically calculate the decision surface that can be used by an application at run-time to quickly choose the optimal path. Finally, we use quantitative error analysis to validate the accuracy of our approach. [ABSTRACT FROM AUTHOR]

Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf. [ABSTRACT FROM AUTHOR]

Macroscopic and microscopic models are typical approaches for simulating crowd behaviour and movement to simulate crowd and pedestrian movement, respectively. However, the two models are unlikely to address the issues beyond their modelling targets (i.e., pedestrian movement for microscopic models and crowd movement for macroscopic models). In order to solve such problem, we propose a hybrid model integrating macroscopic model into microscopic model, which is capable of taking into account issues both from crowd movement tendency and individual diversity to simulate crowd evacuation. In each simulation time step, the macroscopic model is executed first and generates a course-grain simulation result depicting the crowd movement, which directs microscopic model for goal selection and path planning to generate a fine-grain simulation result. In the mean time, different level-of-detail simulation results can also be obtained due to the proposed model containing two complete models. A synchronization mechanism is proposed to convey simulation results from one model to the other one. The simulation results via case study indicate the proposed model can simulate the crowd and agent behaviour in dynamic environments, and the simulation cost is proved to be efficient. [ABSTRACT FROM AUTHOR]

This paper presents an interactive multi-agent system based on a fully immersive virtual environment. A user can interact with the virtual characters in real time via an avatar by changing their moving behavior. Moreover, the user is allowed to select any character as the avatar to be controlled. A path planning algorithm is proposed to address the problem of dynamic navigation of individual and groups of characters in the multi-agent system. A natural interface is designed for the interaction between the user and the virtual characters, as well as the virtual environment, based on gesture recognition. To evaluate the efficiency of the dynamic navigation method, performance results are provided. The presented system has the potential to be used in the training and evaluation of emergency evacuation and other real-time applications of crowd simulation with interaction. [ABSTRACT FROM AUTHOR]

Real-time rendering of large animated crowds consisting of thousands of virtual humans is important for several applications including simulations, games, and interactive walkthroughs but cannot be performed using complex polygonal models at interactive frame rates. For that reason, methods using large numbers of precomputed image-based representations, called impostors, have been proposed. These methods take advantage of existing programmable graphics hardware to compensate for computational expense while maintaining visual fidelity. Thanks to these methods, the number of different virtual humans rendered in real time is no longer restricted by computational power but by texture memory consumed for the variety and discretization of their animations. This work proposes a resource-efficient impostor rendering methodology that employs image morphing techniques to reduce memory consumption while preserving perceptual quality, thus allowing higher diversity or resolution of the rendered crowds. Results of the experiments indicated that the proposed method, in comparison with conventional impostor rendering techniques, can obtain 38 % smoother animations or 87 % better appearance quality by reducing the number of key-frames required for preserving the animation quality via resynthesizing them with up to 92 % similarity on real time. [ABSTRACT FROM AUTHOR]

The social force model (SF) is able to reproduce many emergent phenomena observed in real crowds. Unfortunately, in some situations, such as low density environments, SF may produce counterintuitive results where the trajectories simulated look more like particles than to real people. We modify the SF model through the use of a mobile grid to allow the simulated pedestrians to change the direction of their desired velocity at reasonable times, thus avoiding nearby blocked or crowded areas smoothly. Our experiments focus on qualitative behavior, and verify that our model produces the desired trajectories of the pedestrians, achieving softer and more coherent trajectories when compared to the pure SF model solution. Like SF, our model reproduces the 'faster-is-slower' and the arching underlying the clogging effects. Finally, we examine the occupation rates of the space when pedestrians were submitted to narrowed corridors and observe the 'edge effect.' [ABSTRACT FROM AUTHOR]

Recent algorithms have been able to simulate 'social crowds' that allow agents to interact socially as opposed to only treating other agents as obstacles. Unfortunately, past social crowd algorithms lack realism and flexibility because they do not allow agents to move in and out of different and repeated social interactions, are built around a specific obstacle avoidance algorithm, or are tuned only for a specific social setting and do not allow for artist directed changes. We propose a new, simplified social crowd algorithm that focuses on the evolving social needs of agents and allows each agent to join and leave different encounters as desired. Our algorithm is based on the psychology research area of transactional analysis, does not require a specific obstacle avoidance algorithm, and allows for easy artist direction for determining the precise social environment being simulated. Our algorithm runs in real-time with 3,000 to 4,000 agents without the restrictions of previous research. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for individual agent's motion simulation in real-time virtual environments. In our model, we focus on addressing two problems: 1) the control model for local motions. We propose to represent a combination of psychological and geometrical rules with a social and physical forces model so that it can avoid individual agent's local collision. 2) Global path planning algorithm with moving obstacle. We propose a more efficient algorithm by extending the indicative route method. Experimental results show that the proposed approach can be tuned to simulate different types of crowd behaviors under a variety of conditions, and can naturally exhibit emergent phenomena that have been observed in real crowds. [ABSTRACT FROM AUTHOR]

This paper researches the nonlinear dynamics of the behavior selection networks (BSN) model by virtue of which we can understand the origin of flocking behaviors in social networks. To commentate the notion of BSN, this article introduces a social behavior selection model for evolutionary dynamics of behaviors in social networks that exhibits a rich set of emergent behaviors of evolution. For behavioral networks with different complex networks topology, we analyze the nonlinear dynamics including the chaotic dynamics by the numerical simulation tools. With changing the topological structure, the behavioral networks behave affluent dynamical phenomena. Lastly, we draw the conclusion and paste the prospection about the networks model. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel method for controlling massive crowds by using control particles. Our method differs from previous ones that generate attraction (or repelling) forces around the control particles. Instead of doing this, we create a steady-state, flow-like control field that guides the crowd to move along with the control particles. Our control field can be naturally incorporated into the original simulation by using density-based weighted blending. Although we focus on simulation methods that use dynamic potential functions, our method can also be used to improve the controllability of agent-based simulation methods. Since the control particles can be easily manipulated by traditional key-framing, our method provides animators with an intuitive interface for manipulating the position of crowd over time. We illustrate the effectiveness of our method on several examples. [ABSTRACT FROM AUTHOR]

This paper proposes a rule-based motion planning system for agent-based crowd simulation, consisting of sets of rules for both collision avoidance and collision response. In order to avoid an oncoming collision, a set of rules for velocity sampling and evaluation is proposed, which aims to choose a velocity with an expected time to collision larger than a predefined threshold. In order to improve the efficiency over existing methods, the sampling procedure terminates upon finding an appropriate velocity. Moreover, the proposed motion planning system does not guarantee a collision-free movement. In case of collision, another set of rules is also defined to direct the agent to make a corresponding response. The experiment results show that the proposed approach can be applied in different scenarios, while making the simulation execution efficient. [ABSTRACT FROM AUTHOR]