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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]

Evacuation scene simulation has become one important approach for public safety decision‐making. Although existing research has considered various factors, including social forces, panic emotions, and so forth, there is a lack of consideration of how complex environmental factors affect human psychology and behavior. The main idea of this paper is to model complex evacuation environmental factors from the perspective of knowledge and explore pedestrians' emergency response mechanisms to this knowledge. Thus, a knowledge‐driven dynamic perception model (KDPM) for evacuation scene simulation is proposed in this paper. This model combines three modules: knowledge dissemination, dynamic scene perception, and stress response. Both scenario knowledge and hazard source knowledge are extracted and expressed. The improved intelligent agent perception model is designed by adopting position determination. Moreover, a general adaptation syndrome (GAS) model is first presented by introducing a modified stress system model. Experimental results show that the proposed model is closer to the reality of real data sets. [ABSTRACT FROM AUTHOR]

Crowd evacuation has gained increasing attention in recent years. The agent‐based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this article, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics‐based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to control the agent motion in the motion model. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of the crowd evacuation simulation demonstrate that our framework can simulate crowd evacuation with physical fidelity. [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]

In crowd simulation, representing crowd behavior in complex dynamic environments is one of the biggest challenges. In this paper, we propose new algorithms to make crowds satisfy a given formation while they are moving towards a destination. For this, we apply the Position Based Dynamics (PBD) framework, but introduce a new formation constraint based on a so-called Short Range Destination (SRD). The SRD is a short-term goal to which an agent must move in formation. In addition, a grid structure that we use for neighbor search is also used for congestion control. Depending on the congestion value, the agents in the cell may break the formation and instead exhibit emergent behaviors such as collision avoidance, but must automatically restore the original formation once the situation is resolved. Smooth movement of agents is also achieved by adding special behaviors when they are moving along the path that the user specifies. From several experiments, we show that the proposed scheme is capable of exhibiting natural aggregate behavior of crowds in real time, even for a highly condensed environment. [ABSTRACT FROM AUTHOR]

Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or cognition levels, emotional contagion evolution, and the regulation mechanism of pedestrians to negative emotion, are still rare. In this paper, an evacuation model based on emotional cognition and contagion (EMECC) is presented. Firstly, individual's emotion is generated and quantified based on Lazarus's cognitive theory. Secondly, the emotional contagion between individuals is simulated by SIS (Susceptible Infected Susceptible) infectious disease model. Combining with cellular automata model, an emotion-driven moving rule is proposed to guide pedestrians move towards the directions with more positive individuals so that positive emotions can be spread effectively. Various experiments on model parameters, obstacles, and emotional contagion process are implemented to verify the effectiveness of the EMECC model. The simulation and experimental results show that emotional regulation mechanism can improve pedestrian's decision-making ability and contagion of positive emotion can accelerate evacuation process. The EMECC model can simulate emotional changes dynamically and guide pedestrians efficiently and reasonably in emergency evacuation. [ABSTRACT FROM AUTHOR]

Managing crowds is important not only during evacuation in disasters such as earthquakes and fires but also during normal situations. In particular, places where many people gather every day, such as stations or event venues, need such management to prevent crowd accidents. Moreover, efficient guidance that prevents people from waiting or queuing can improve facility services and lead to business opportunities. In this study, we propose a crowd management platform to prevent crowd accidents and provide efficient guidance to visitors. Specifically, we integrate real-time observations of crowd conditions, predictions, and risk assessments through simulation and crowd control in collaboration with security and facility managers. We also present the results of operating this platform in actual fields, which contribute to and support the safety and comfort of individuals. [ABSTRACT FROM AUTHOR]

To prevent crowd incidents, it is important to share information among stakeholders from the planning stage of crowd management. However, numerous stakeholders are typically involved in crowd management for events, and it is difficult for each stakeholder to understand and agree on their goals, roles, responsibilities, and plans. Additionally, given the vast number of scenarios, it is also difficult to consolidate plans in an efficient and effective manner. Therefore, we devised the simulation-based agile approach (SBAA) methodology, which is characterized by "collaboration with stakeholders," "responding to plan changes," and "identifying quality requirements for operations through iterative proposals and agreements." The first two characteristics are consistent with the Agile Manifesto, which has proven successful as a software development methodology. We participated in the creating of a crowd control plan for a local fireworks display and put SBAA into practice. The SBAA methodology and the effectiveness of SBAA in practice are discussed herein. [ABSTRACT FROM AUTHOR]

The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors. [ABSTRACT FROM AUTHOR]

Achieving crowd formation transformations has wide‐ranging applications in fields such as unmanned aerial vehicle formation control, crowd simulation, and large‐scale performances. However, planning trajectories for hundreds of agents is a challenging and tedious task. When modifying crowd formation change schemes, adjustments are typically required based on the style of formation change. Existing methods often involve manual adjustments at each crucial step, leading to a substantial amount of physical labor. Motivated by these challenges, this study introduces a novel generative adversarial network (GAN) for generating crowd formation transformations. The proposed GAN learns specific styles from a series of crowd formation transformation trajectories and can transform a new crowd with an arbitrary number of individuals into the same styles with minimal manual intervention. The model incorporates a space–time transformer module to aggregate spatiotemporal information for learning distinct styles of formation transformation. Furthermore, this article investigates the relationship between the distribution of training data and the length of trajectory sequences, providing insights into the preprocessing of training data. [ABSTRACT FROM AUTHOR]

The spatial layout of hospitals shows constant intersection of streamlines with the patients in different kinds of needs finding their path to various service facilities. Along with COVID pandemic, low-trust psychology raises among social relationships, which has brought significant changes during space planning process. This paper aimed at the lack of specific technical methods for flow simulation and spatial layout for hospital buildings. To simulate hospital crowd behaviors, this paper first proposes a novel low-trust social force model. Then, based on medical process analysis, hospital space survey, and infection theory, the simulation model was established. After that, the original and planned layouts were simulated, and key performance metrics were calculated. Results are visualized and the impact of different forms of hospital space layout and service facility layout are analyzed. Finally, optimization suggestions are proposed, which can provide a basis for hospital space and facility layout evaluation while reducing the cost of frequent hospital renovations. Based on computer simulation with new LtSFM, this method has the advantage of accurate patient crowd prediction and lead to effective space layout planning. Applications in a real-world hospital showed that the proposed method can predict the bottleneck of the layout capacity of hospital space facilities and propose corresponding improvement measures, thereby reducing the hidden risk of passenger flow gathering, improving the service level of facilities, and reducing overall infection risks.

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under ‘normality’ to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications.

Achieving crowd formation transformations has wide‐ranging applications in fields such as unmanned aerial vehicle formation control, crowd simulation, and large‐scale performances. However, planning trajectories for hundreds of agents is a challenging and tedious task. When modifying crowd formation change schemes, adjustments are typically required based on the style of formation change. Existing methods often involve manual adjustments at each crucial step, leading to a substantial amount of physical labor. Motivated by these challenges, this study introduces a novel generative adversarial network (GAN) for generating crowd formation transformations. The proposed GAN learns specific styles from a series of crowd formation transformation trajectories and can transform a new crowd with an arbitrary number of individuals into the same styles with minimal manual intervention. The model incorporates a space–time transformer module to aggregate spatiotemporal information for learning distinct styles of formation transformation. Furthermore, this article investigates the relationship between the distribution of training data and the length of trajectory sequences, providing insights into the preprocessing of training data.

Crowd simulation is a research area widely used in diverse fields, including gaming and security, assessing virtual agent movements through metrics like time to reach their goals, speed, trajectories, and densities. This is relevant for security applications, for instance, as different crowd configurations can determine the time people spend in environments trying to evacuate them. In this work, we extend WebCrowds, an authoring tool for crowd simulation, to allow users to build scenarios and evaluate them through a set of metrics. The aim is to provide a quantitative metric that can, based on simulation data, select the best crowd configuration in a certain environment. We conduct experiments to validate our proposed metric in multiple crowd simulation scenarios and perform a comparison with another metric found in the literature. The results show that experts in the domain of crowd scenarios agree with our proposed quantitative metric.

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods. [ABSTRACT FROM AUTHOR]

In crowd simulation, representing crowd behavior in complex dynamic environments is one of the biggest challenges. In this paper, we propose new algorithms to make crowds satisfy a given formation while they are moving towards a destination. For this, we apply the Position Based Dynamics (PBD) framework, but introduce a new formation constraint based on a so-called Short Range Destination (SRD). The SRD is a short-term goal to which an agent must move in formation. In addition, a grid structure that we use for neighbor search is also used for congestion control. Depending on the congestion value, the agents in the cell may break the formation and instead exhibit emergent behaviors such as collision avoidance, but must automatically restore the original formation once the situation is resolved. Smooth movement of agents is also achieved by adding special behaviors when they are moving along the path that the user specifies. From several experiments, we show that the proposed scheme is capable of exhibiting natural aggregate behavior of crowds in real time, even for a highly condensed environment.

Simulating crowds with realistic behaviors is a difficult but very important task for a variety of applications. Quantifying how a person balances between different conflicting criteria such as goal seeking, collision avoidance and moving within a group is not intuitive, especially if we consider that behaviors differ largely between people. Inspired by recent advances in Deep Reinforcement Learning, we propose Guided REinforcement Learning (GREIL) Crowds, a method that learns a model for pedestrian behaviors which is guided by reference crowd data. The model successfully captures behaviors such as goal seeking, being part of consistent groups without the need to define explicit relationships and wandering around seemingly without a specific purpose. Two fundamental concepts are important in achieving these results: (a) the per agent state representation and (b) the reward function. The agent state is a temporal representation of the situation around each agent. The reward function is based on the idea that people try to move in situations/states in which they feel comfortable in. Therefore, in order for agents to stay in a comfortable state space, we first obtain a distribution of states extracted from real crowd data; then we evaluate states based on how much of an outlier they are compared to such a distribution. We demonstrate that our system can capture and simulate many complex and subtle crowd interactions in varied scenarios. Additionally, the proposed method generalizes to unseen situations, generates consistent behaviors and does not suffer from the limitations of other data-driven and reinforcement learning approaches. [ABSTRACT FROM AUTHOR]

This research incorporates Bayesian game theory into pedestrian evacuation in an agent-based model. Three pedestrian behaviours were compared: Random Follow, Shortest Route and Bayesian Nash Equilibrium (BNE), as well as combinations of these. The results showed that BNE pedestrians were able to evacuate more quickly as they predict congestion levels in their next step and adjust their directions to avoid congestion, closely matching the behaviours of evacuating pedestrians in reality. A series of simulation experiments were conducted to evaluate whether and how BNE affects pedestrian evacuation procedures. The results showed that: 1) BNE has a large impact on reducing evacuation time; 2) BNE pedestrians displayed more intelligent and efficient evacuating behaviours; 3) As the proportion of BNE users rises, average evacuation time decreases, and average comfort level increases. A detailed description of the model and relevant experimental results is provided in this paper. Several limitations as well as further works are also identified. [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]

With the extensive application of data analysis in various parts of the landscape design process, Building Information Modeling (BIM), City Information Modeling (CIM), and Landscape Information Modeling (LIM) all aim to achieve key data sharing and collaboration in the whole cycle and promote the development of smart cities. Landscape element indicators are complex, diverse, and difficult to quantify, which is one of the reasons for the slow development of LIM. However, with the development requirements of LIM, quantifying landscape elements and transforming landscapes into digital landscape information has become a hot spot in the landscape design industry. Landscape parametric design aims to transform the design elements into quantifiable parameters, obtain the design scheme by changing the value of the parameters, and form the design results based on digital information. It is one of the foundations of LIM. Based on the Rhino + Grasshopper (R+G) platform, this study takes Shuixizhuang Park as an example and establishes the parametric design approach for the park entrance. The approach involves several steps: (1) Confirming the boundary and key points of the park to prepare the basic data for parametric design. (2) Utilizing the Physarealm Algorithm Method (PAM) to simulate crowd paths, the Site Slope Algorithm Model (SSAM) to analyze terrain slope, the Road Intersection Algorithm Model (RIAM) to determine the scope of the park entrance area based on the national and local design codes, and the Crowd Density Algorithm Model (CDAM) and Crowd Convenience Algorithm Model (CCAM) to analyze the density and convenience of the crowd to preliminarily confirm the park entrance. (3) Meeting the basic requirements of the crowd and vehicle gathering and spread by using the Square Area Review Algorithm Model (SARAM) and Parking Lot Review Algorithm Model (PLRAM) to recheck the site area of the park entrance square and park lot to optimize the park entrance. This approach constructs several site analysis models based on the R+G platform and introduces PAM to analyze crowd activity paths, proposing a landscape parametric design method that integrates crowd activity and landscape design requirements. Compared with the classical design, the landscape parametric design derived from the comprehensive data analysis reduces human interference, is more scientific and practical, and better meets the requirements of people entering the park. The approach also provides ideas for other landscape parametric site selections. By adjusting the values of element parameters, the approach can also be applied to the site selection and design of other landscapes. [ABSTRACT FROM AUTHOR]

Due to the complex and subtle behaviors of humans, realistic crowd simulation is difficult. To that end, we propose a novel crowd simulation method that can generate realistic crowd animations with behaviors similar to real crowds and model complex pedestrian behaviors at multiple levels using social long short‐term memory (LSTM) neural networks. At the high level, our multi‐level simulation model provides global group navigation while at the low level, it can simulate local individual interactions with collision avoidance. We introduce a data‐driven method using an improved social LSTM for learning local motion decisions from real pedestrian trajectories in order to capture the subtle movements of the crowd. To achieve scalability, we formulate the low‐level and high‐level motion control in a force‐based scheme. Extensive simulation results demonstrate that our method can produce realistic crowd animations in a variety of scenarios. Evaluations in various metrics show that our method produces better crowd behaviors than previous methods. [ABSTRACT FROM AUTHOR]

In this article, we propose an integrated model for simulating the interaction between crowds and fluid particles. Our focus is on simulating evacuation motion for crowds in the face of sudden floods. Our model treats both the crowd and the water as fluid particles, which allows us to incorporate various forces such as pressure, shear, buoyancy, and active forces to drive the agents. Additionally, we have designed a minimum rotational path‐planning algorithm for agents to search for safe destinations during evacuations. To develop practical crowd evacuation strategies, we observed and studied survival techniques from whirlpools and sudden changes in water levels during floods. Our simulated evacuation results provide plausible strategies for crowds to survive dangerous floods. [ABSTRACT FROM AUTHOR]

Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents' behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues. [ABSTRACT FROM AUTHOR]

Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents' information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work. [Display omitted] • Crowd simulation details affect the results when trained with reinforcement learning. • It is typically beneficial for agents to directly observe others' positions. • Egocentric observations and actions are better than global ones. • Reward design is highly impactful and nontrivial. • Simple reward functions do not produce human-like behavior. [ABSTRACT FROM AUTHOR]

More cautious fire safety evacuation assessment and escape route assistance are required when people with disabilities encounter a fire hazard in a historical museum. This study uses the old Chiayi City Hall, which is mainly used for an exhibition space, as the example. The core of this study is the problem of fire evacuation that emerged after the historic building opened and was reused as a museum; it uses Pathfinder evacuation simulation software, discussing the difference between traditional and segregated evacuation and assuming the original evacuation, elevator emergency evacuation, and external ramp evaluation. There are three evacuation scenarios, and the number of accommodated people is set to 730, pursuant to the applicable law. Comparing the conditions of people with disabilities and ordinary members of the public during evacuation through the foregoing three scenarios, the overall evacuation time and survival rate of the original evacuation scenario are 440 s and 49.8%, respectively; and the overall evacuation time and survival rate of the barrier-free elevator emergency evacuation scenario are 332 s and 65.4%, respectively; the overall evacuation time and survival rate of the external ramp evaluation scenario are 320 s and 65.6%, respectively. The computer data analysis shows that the use of the external ramp evaluation gives people with disabilities the best evacuation time and survival rate because the architectural form of cultural heritage buildings is more fragile, specific, and fast-burning than that of ordinary buildings. As the global awareness of cultural asset preservation and revitalization is increasing, the evacuation of people with disabilities in the building in the case of fire is very important. The results of this study can be used as an emergency evacuation design recommendation for people with disabilities in the cultural assets through evacuation simulation analysis. [ABSTRACT FROM AUTHOR]

The role of the leader in the self‐organizing crowd has a significant influence on the crowd evacuation when an emergency occurs. However, current crowd evacuation models either ignore the leadership characteristics of pedestrians or predefine an invariable leader identity. This article clearly distinguishes leaders from followers in a crowd and evaluates the impact of the leader's role on crowd evacuation under dynamic situations. First, the leadership model of the pedestrian is built based on the classic OCEAN personality parameters. Social dominance tendency is measured for more credibility. Then, the dynamic leader role model (DLRM) is proposed to describe the relationship between leaders and followers, considering the interaction among pedestrians during the evacuation. Finally, a novel crowd simulation algorithm based on the above DLRM is presented using the extended social force model. Various experiments in several typical scenarios verified that our proposed simulation model has better realism. [ABSTRACT FROM AUTHOR]

Realistic crowd simulation has always been an important research field in computer graphics. While both agent‐based motion models and data‐driven behavior models have made some progress, they are still suffering from either huge effort of multi‐parameter tuning or limited realistic motion. In this article, we propose a novel and differentiable multi‐parameter learning method for crowd simulation, which is called ORCANet. The main idea is to learn from real data and inverse evaluating the multi‐parameter for subsequent simulation. ORCANet uses classic optimal reciprocal collision avoidance (ORCA) as a basic motion model which is integrated into the deep learning framework. Addressing the feature of linear programming and non‐differentiable operation, a Gaussian kernel is added to approximate the role of neighbor distance in collision avoidance, which turns the original discrete operation into a fully differentiable forward simulation. Furthermore, we leverage ORCANet to optimize the multi‐parameter combination in synthetic and real‐world datasets. ORCANet is proved to rapidly converge to correct parameter values and regenerate the input synthetic sequence. Moreover, experiments on real‐world datasets by the metric of pedestrian trajectories verified that a more realistic crowd simulation has been generated through ORCANet. [ABSTRACT FROM AUTHOR]

Virtual Humans (VHs) emerged over 50 years ago and have since experienced notable advancements. Initially, developing and animating VHs posed significant challenges. However, modern technology, both commercially available and freely accessible, has democratized the creation and animation processes, making them more accessible to users, programmers, and designers. These advancements have led to the replication of authentic traits and behaviors of real actors in VHs, resulting in visually convincing and behaviorally lifelike characters. As a consequence, many research areas arise as functional VH technologies. This paper explored the evolution of four areas and emerging trends related to VHs while examining some of the implications and challenges posed by highly realistic characters within these domains. [Display omitted] • We present a detailed foundation and history of Virtual Humans. • We conducted a literature review on four relevant areas for Virtual Humans • Areas are: Perception; Facial Transferring; Conversational Agents; and Crowd Simulation. • We discuss the main challenges still present in the four mentioned areas. [ABSTRACT FROM AUTHOR]

The integrated use of building information modeling (BIM) and geographic information system (GIS) is promising for the development of asset management systems (AMSs) for operation and maintenance (O&M) in smart university campuses. The combination of BIM-GIS with cognitive digital twins (CDTs) can further facilitate the management of complex systems such as university building stock. CDTs enable buildings to behave as autonomous entities, dynamically reacting to environmental changes. Timely decisions based on the actual conditions of buildings and surroundings can be provided, both in emergency scenarios or when optimized and adaptive performances are required. The research aims to develop a BIM-GIS-based AMS for improving user experience and enabling the optimal use of resources in the O&M phase of an Italian university. Campuses are complex assets, mainly diffused with buildings spread across the territory, managed with still document-based and fragmented databases handled by several subjects. This results in incomplete and asymmetrical information, often leading to ineffective and untimely decisions. The paper presents a methodology for the development of a BIM-GIS web-based platform (i.e., AMS-app) providing the real-time visualization of the asset in an interactive 3D map connected to analytical dashboards for management support. Two buildings of the University of Turin are adopted as demonstrators, illustrating the development of an easily accessible, centralized database by integrating spatial and functional data, useful also to develop future CDTs. As a first attempt to show the AMS app potential, crowd simulations have been conducted to understand the buildings’ actual level of safety in case of fire emergency and demonstrate how CDTs could improve it. The identification of data needed, also gathered through the future implementation of suitable sensors and Internet of Things networks, is the core issue together with the definition of effective asset visualization and monitoring methods. Future developments will explore the integration of artificial intelligence and immersive technologies to enable space use optimization and real-time wayfinding during evacuation, exploiting digital tools to alert and drive users or authorities for safety improvement. The ability to easily optimize the paths with respect to the actual occupancy and conditions of both the asset and surroundings will be enabled.

Crowd evacuation has gained increasing attention due to its importance in the day-to-day management of public areas. During an emergency evacuation, there are a variety of factors that need to be considered when designing a practical evacuation model. For example, relatives tend to move together or look for each other. These behaviors undoubtedly aggravate the chaos degree of evacuating crowds and make evacuations hard to model. In this paper, we propose an entropy-based combined behavior model to better analyze the influence of these behaviors on the evacuation process. Specifically, we utilize the Boltzmann entropy to quantitatively denote the degree of chaos in the crowd. The evacuation behavior of heterogeneous people is simulated through a series of behavior rules. Moreover, we devise a velocity adjustment method to ensure the evacuees follow a more orderly direction. Extensive simulation results demonstrate the effectiveness of the proposed evacuation model and provide useful insights into the design of practical evacuation strategies. [ABSTRACT FROM AUTHOR]

Crowd formation transformation is the process of controlling crowd movement under high constraints, which has received massive attention in recent years due to its ability to generate high‐quality visual effects. It is challenging because of the complexity and the lack of evaluation standard. This study introduces an anchor‐based crowd formation transformation control method that achieves highly controllable crowd movement by extracting anchors and optimizing a loss function based on anchor constraints. In addition, we propose a series of metrics to assist users in quantifying a segment of group motion. Some cases not considered in past work are designed to demonstrate the superiority of this method. With the help of proposed metrics, it could be found that in addition to being able to uniquely accomplish some tasks, our method also exhibits higher control over the crowd in other scenarios. Compared with methods of other research, our method has significant advantages in diversity and controllability of the generated results. [ABSTRACT FROM AUTHOR]

In this study, we propose CoR-SketchAR, an augmented reality (AR) environment authoring tool that uses dry-erase markers for real-time collaboration among users. The most important requirement for multi-user collaboration in an AR environment is that the 3D virtual object must be placed at a specific location and can be easily changed by multiple users. Because marker-based registration techniques, which are widely used for matching virtual objects with real ones, require a marker for each object, and creating a crowd simulation environment with objects of various shapes and sizes requires the use of a large number of markers, which is time consuming and expensive. CoR-SketchAR, instead, creates an urban AR environment by drawing sketches with easily altered dry-erase marker. Then, system recognizes the shapes and colors of the sketches automatically. Those recognized shapes and colors provide the exact positions for overlaying the 3D virtual objects, which are the environment factors, on the real environment in augmented reality manner. We can even specify the path the crowd are moving along with a simple sketch stroke. We apply the computer vision technique to recognize the colors and shapes of sketches. By altering the size, shape and color sketches, the system is able to create a wide variety of dynamic urban environments. To validate the proposed techniques, we built two stand-alone software systems to check the usability of the proposed system (a 2D screen-based environmental authoring tool and a sketch-based environmental authoring tool) and conducted experiments in which two users collaborated with each other to create an environment with a specific authoring tool and then report surveys. In the experiments, users collaborated in pairs to create environmental elements, such as highways, buildings, trees, and the starting and goal positions of crowds. After recognizing them, the system then automatically creates a 3D environment, and crowds are animated accordingly. Based on a user survey, we observed that participants who used sketch-based environmental authoring tools were more active and accessible than those who used 2D screen-based authoring tools. The results of the study show that CoR-SketchAR can be further used to create a dynamical crowd simulation on a large scale using beam projectors or portable devices by simply adding sketches based on different scenarios. [ABSTRACT FROM AUTHOR]

Crowd simulation is a challenging problem, aiming to generate realistic pedestrians motions in virtual environment. Nowadays, ORCA is a widely used simulation algorithm in practice because of its stable and efficient performance. However, this algorithm cannot regenerate continuity and diversity of pedestrian motions in real data, leading to defects in motion fidelity. Otherwise, trajectory prediction methods based on deep learning have progressed in real pedestrians movement patterns mining. However, they are rarely applied in simulation due to the lack of ability to avoid collision and adapt to manufactured scenarios. Our work proposes a simulation method DeepORCA that integrates ORCA with a CVAE‐based velocity probability generator, which can model motion continuity, variable intentions, and scene semantics. Moreover, DeepORCA converts the velocity optimization into quadratic programming, which accelerates the calculation while maintaining the collision‐avoidance ability of ORCA. In the experiments of real and artificial scenes, our method produces more realistic crowd simulation results than ORCA quantitatively and qualitatively, while keeps the computational efficiency at the same order of magnitude. [ABSTRACT FROM AUTHOR]

We propose a virtual crowd navigation approach based on deep reinforcement learning to improve the adaptability of virtual crowds in an unknown and complex environment. To address the problem of local optimum or slow iteration or even failure to converge due to sparse rewards in complex environments, we integrate the curiosity‐driven mechanism, the key navigation points acquisition and the failure path penalty method in addition to combining long short‐term memory networks, dynamic obstacle collision prediction with proximal policy optimization algorithms, which realizes the crowd navigation in a complex environment. The experimental results show that the proposed approach can simulate the motions of virtual crowds in various dynamic and complex scenarios without the environment modeling. The use of continuous action space also ensures that the movement trajectories of the virtual crowds are more realistic and natural. Furthermore, our approach can provide analysis and demonstration tools for a variety of independent collaborations such as competition and cooperation of group intelligence in an open and dynamic environment. [ABSTRACT FROM AUTHOR]

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

With the extensive application of data analysis in various parts of the landscape design process, Building Information Modeling (BIM), City Information Modeling (CIM), and Landscape Information Modeling (LIM) all aim to achieve key data sharing and collaboration in the whole cycle and promote the development of smart cities. Landscape element indicators are complex, diverse, and difficult to quantify, which is one of the reasons for the slow development of LIM. However, with the development requirements of LIM, quantifying landscape elements and transforming landscapes into digital landscape information has become a hot spot in the landscape design industry. Landscape parametric design aims to transform the design elements into quantifiable parameters, obtain the design scheme by changing the value of the parameters, and form the design results based on digital information. It is one of the foundations of LIM. Based on the Rhino + Grasshopper (R+G) platform, this study takes Shuixizhuang Park as an example and establishes the parametric design approach for the park entrance. The approach involves several steps: (1) Confirming the boundary and key points of the park to prepare the basic data for parametric design. (2) Utilizing the Physarealm Algorithm Method (PAM) to simulate crowd paths, the Site Slope Algorithm Model (SSAM) to analyze terrain slope, the Road Intersection Algorithm Model (RIAM) to determine the scope of the park entrance area based on the national and local design codes, and the Crowd Density Algorithm Model (CDAM) and Crowd Convenience Algorithm Model (CCAM) to analyze the density and convenience of the crowd to preliminarily confirm the park entrance. (3) Meeting the basic requirements of the crowd and vehicle gathering and spread by using the Square Area Review Algorithm Model (SARAM) and Parking Lot Review Algorithm Model (PLRAM) to recheck the site area of the park entrance square and park lot to optimize the park entrance. This approach constructs several site analysis models based on the R+G platform and introduces PAM to analyze crowd activity paths, proposing a landscape parametric design method that integrates crowd activity and landscape design requirements. Compared with the classical design, the landscape parametric design derived from the comprehensive data analysis reduces human interference, is more scientific and practical, and better meets the requirements of people entering the park. The approach also provides ideas for other landscape parametric site selections. By adjusting the values of element parameters, the approach can also be applied to the site selection and design of other landscapes.

Pedestrian modeling and simulation has become an interesting topic that has gained scientists' interest over the past few decades. While this field has yielded significant achievements in various applications, questions have been raised regarding the applicability of simulations in high-density scenarios. To this purpose, this paper comprehensively reviews pedestrian simulation models specifically focused on high-density situations. The review examines a total of 116 articles and categorizes their approaches for modeling pedestrian behaviors to different decisional levels. The strengths and limitations of these modeling approaches are compared and evaluated using different criteria for dense crowd simulations, such as their ability to simulate common emergent behaviors in crowded situations, performance, validation, and capacity to integrate into high-level modeling. Finally, the review provides potential directions for future research and development of dense crowd simulations. • Conducting a systematic review of existing approaches for dense crowd simulation. • Investigating pedestrian simulation models at different behavioral levels. • Comparing various models using multiple criteria tailored to high-density situations. • Analyzing the advantages and drawbacks of simulation models in crowded settings. [ABSTRACT FROM AUTHOR]

Modelling visual attention is an important aspect of simulating realistic virtual humans. This work proposes a parametric model and method for generating real-time saliency maps from the perspective of virtual agents which approximate those of vision-based saliency approaches. The model aggregates a saliency score from user-defined parameters for objects and characters in an agent's view and uses that to output a 2D saliency map which can be modulated by an attention field to incorporate 3D information as well as a character's state of attentiveness. The aggregate and parameterized structure of the method allows the user to model a range of diverse agents. The user may also expand the model with additional layers and parameters. The proposed method can be combined with normative and pathological models of the human visual field and gaze controllers, such as the recently proposed model of egocentric distractions for casual pedestrians that we use in our results. This is an extended version of a short paper published in MIG 2021. The extension includes an optimization approach that fits the parameters of the proposed model to established saliency models such as SALICON using a much larger and more realistic urban test set. [Display omitted] [ABSTRACT FROM AUTHOR]

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method's ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents' navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions. [ABSTRACT FROM AUTHOR]

Hajj is a mass gathering event that takes place annually in Makkah, Saudi Arabia. Typically, around three million people participate in the event and perform rituals that involve their movements within strict space and time restrictions. Despite efforts by the Hajj organisers, such massive crowd gathering and movement cause overcrowding related problems at the Hajj sites. Several previous simulation studies on Hajj focused on the rituals individually. Tawaf, followed by Sayee, are two important rituals that are performed by all the pilgrims at the same venue on the same day. These events have a strong potential for crowd buildup and related problems. As opposed the previous works in the literature, in this paper we study these two events jointly, rather than separately. We use ExtendSim, a Discrete Event Simulation tool, to integrate the Tawaf and Sayee rituals into one model. The validated model was applied to a wide range of scenarios where different percentages of pilgrims were allocated to the various Tawaf and Sayee areas. The effect of such allocations on the time to complete Tawaf and Sayee indicate strategies for managing these two key Hajj rituals.

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed. [ABSTRACT FROM AUTHOR]

Taking into consideration the behavior of social groups, such as groups of friends, couples and families, is very important in crowd simulation. Existing approaches of group modeling offer little flexibility for customizing the structure of the groups and the relationships between their members, which limits their representation power. Often, those approaches also have limitations regarding their ability to represent groups of arbitrary sizes, as well as to represent certain aspects of the groups' cohesion, such as the ability of modeling how their members will walk together and whether they will follow the same routes. This work proposes a way of modeling groups with different structures, in which roles of leadership can be assigned to some of their members. The approach can also represent groups of arbitrary sizes that may consist of subgroups, and treat different levels of relationships between their members. Moreover, the groups can be modeled in such a way that their members show either a strong cohesion with one another or a loose cohesion, in which the members behave more autonomously when dealing with collision avoidance and choice of routes. Nevertheless, the sense of group is always present as the model offers the ability of regrouping whenever one or more members of a group are left behind. The model also includes a new personality factor that represents the urge of the group members to move towards their destinations when they experience a strong impedance from the crowd. The approach is based on states that the pedestrians can assume based on their profiles and on the social forces model. [Display omitted] • Model capable of representing groups with flexible structures and relationships. • Model with flexibility of group cohesion. • New personality factor that represents a member's urge to reach its goal. [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]

Crowd formation of aesthetic transformation is considered to have extremely high artistic value and is widely applied in large-scale performances. In this paper, a spatio-temporal hierarchical model that parts the crowd formation transform into multiple granularities is proposed. Its core idea is to add spatio-temporal constraints created by directors into transformation process after multi-level division. In this model, average hash value and energy optimization are used to achieve reasonable crowd formation arrangement, while smooth and collision-free formation transformations are presented by constrained region growth and Kuhn-Munkres algorithm. We have also proposed a framework to achieve the generation of visually pleasing crowd formation transform performance based on the constraints. Besides, a virtual crowd formation transformation simulation was built to verify the effect of the proposed model. Through simulation experiments and comparisons, it was demonstrated that this hierarchical model can generate aesthetic crowd formation transformation with a satisfactory process.

Crowd management is a flourishing, active research area and must be given attention due to the potential losses, disasters, and accidents that could occur if it were neglected. For the last decade, the crowd management field has witnessed significant advancements; however, more investigative work is still needed. The integration of different crowd detection and monitoring techniques can enhance the control and the performance compared to those of more limited stand-alone techniques. Crowd management encompasses an entire process, from the monitoring stage through the decision support system stage. This sector involves accessing and interpreting information sources, predicting crowd behavior, and deciding on the use of a range of possible interventions based on context. This paper shows a fresh conclusive review of the concept of the crowd, discussing it from several perspectives in light of its defining characteristics, its risks, and tragedies, which may occur due to challenges faced during crowd management, where these conclusions are based on a massive number of scholarly articles that were newly published. Besides, a systematic discussion is shown concerning the steps of managing a crowd, including crowd detection, in which several new methods are reviewed, followed by illustrating both direct and indirect approaches to crowd monitoring and tracking monitoring. The primary purpose of this review is to establish a comprehensive understanding of crowd-related processes. Moreover, it aims to find research gaps to overcome the limitations of using stand-alone techniques in each process and provide support to other researchers' future work.