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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]

A rapidly growing body of experimental evidence in the literature shows that the effects of humans interacting with vibrating structures, other humans, and their surrounding environment can be critical for reliable estimation of structural vibrations. The Interaction-based Vibration Serviceability Assessment framework (I-VSA) was proposed by the authors in 2017 to address this, taking into account human-structure dynamic interactions (HSI) to simulate the structural vibrations experienced by each occupant/pedestrian. The I-VSA method, however, had limited provisions to simulate simultaneously multiple modes of structure in HSI, to simulate human-human and human-environment interactions, and the movement pattern of the occupants/pedestrians. This study proposes a new Agent-based Vibration Serviceability Assessment framework, termed AVSA, to address the following limitations: (a) allowing for multiple modes of structure to be simulated simultaneously, (b) to simulate effects of vibrations on gait parameters and walking pattern/routes, and (c) to simulate human-environment interactions, and movement patterns for any desired interior layout and use case. The AVSA framework was used to simulate the response and to assess the vibration serviceability of a lightweight floor under a combination of sitting and walking traffic, where three vertical modes of vibrations were engaged simultaneously. The results of the simulations show that for all tests, the experimental Cumulative Distribution Functions of the vibrations experienced by the participants are within the 95% confidence interval predicted by the AVSA method. The proposed method provides a generic and flexible framework to simulate simultaneously different interaction modalities, different human tasks and postures, and multiple modes of structure and the human body. [ABSTRACT FROM AUTHOR]

This paper presents a crowd-density simulation system developed using the Unity engine to predict and prevent crowd-crush accidents. Crowd-crush accidents can occur in various environments where large crowds gather, often leading to significant casualties. Effective tools for risk assessment, training, and decision-making are crucial in preventing such accidents. The proposed simulator aims to provide a realistic virtual environment that accurately models crowd behavior and dynamics. By analyzing crowd density, flow patterns, and bottleneck situations, the simulator enables users to identify potential hazards, evaluate evacuation strategies, and develop proactive crowd management measures. The system incorporates advanced features such as real-time crowd visualization, interactive scenario creation, and data analytics. Validation experiments demonstrate the effectiveness of the simulator in predicting crowd-crush risks and supporting decision-making processes. The proposed solution enhances crowd safety and helps prevent accidents in various public-gathering scenarios.Article Highlights: The Unity-based simulator predicts crowd-crush risks in various urban scenarios with high accuracy Real-time density visualization allows for the rapid identification of high-risk areas, enabling proactive management The modular design enables flexible testing of crowd safety strategies across diverse environmental settings. [ABSTRACT FROM AUTHOR]

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground-truth motion trajectories and struggle with authenticity, especially in high-density groups with erratic movements. In this paper, we present a scalable approach, Collective Behavior Imitation Learning (CBIL), for learning fish schooling behavior directly from videos, without relying on captured motion trajectories. Our method first leverages Video Representation Learning, in which a Masked Video AutoEncoder (MVAE) extracts implicit states from video inputs in a self-supervised manner. The MVAE effectively maps 2D observations to implicit states that are compact and expressive for following the imitation learning stage. Then, we propose a novel adversarial imitation learning method to effectively capture complex movements of the schools of fish, enabling efficient imitation of the distribution of motion patterns measured in the latent space. It also incorporates bio-inspired rewards alongside priors to regularize and stabilize training. Once trained, CBIL can be used for various animation tasks with the learned collective motion priors. We further show its effectiveness across different species. Finally, we demonstrate the application of our system in detecting abnormal fish behavior from in-the-wild videos. [ABSTRACT FROM AUTHOR]

Evacuation systems are crucial for minimizing casualties and property losses during emergencies. Understanding evacuee behavior in overcrowded situations is essential for developing effective evacuation strategies. However, evacuating large crowds from buildings with multiple exits is a challenging problem, especially when the exits are asymmetrical and the crowds are dense. This paper proposes a novel two-dimensional cellular automaton model for multi-exit evacuation, which simplifies evacuee decision-making in an asymmetrical exit layout within dense crowds. The model introduces the dynamic counting area technique, which dynamically adjusts the size and shape of the counting area around each exit based on the evacuee density level. This technique plays a crucial role in preventing the creation of overlapping counting areas between exits, which often leads to overestimated average evacuation time, unit evacuation time, and travel distance. Comparative analysis with previous dynamic parameter models (DPM) reveals notable results: the model achieved an average evacuation time of 201.20 time steps, a unit evacuation time of 0.50 time steps, and a travel distance of 28204 steps. These findings demonstrate the effectiveness of the improved model in addressing evacuation imbalances caused by asymmetrical exit layouts or evacuee distributions. Moreover, the study highlights the pivotal role of evacuee density around exits in determining exit choices during densely crowded emergency situations. The improved model can be applied to various scenarios and settings where multi-exit evacuation is required, such as stadiums, airports, or shopping malls.

Abstract This paper presents a crowd-density simulation system developed using the Unity engine to predict and prevent crowd-crush accidents. Crowd-crush accidents can occur in various environments where large crowds gather, often leading to significant casualties. Effective tools for risk assessment, training, and decision-making are crucial in preventing such accidents. The proposed simulator aims to provide a realistic virtual environment that accurately models crowd behavior and dynamics. By analyzing crowd density, flow patterns, and bottleneck situations, the simulator enables users to identify potential hazards, evaluate evacuation strategies, and develop proactive crowd management measures. The system incorporates advanced features such as real-time crowd visualization, interactive scenario creation, and data analytics. Validation experiments demonstrate the effectiveness of the simulator in predicting crowd-crush risks and supporting decision-making processes. The proposed solution enhances crowd safety and helps prevent accidents in various public-gathering scenarios.

Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places. However, existing methods of the guardrail layout mainly rely on manual design or mathematical models, which are not flexible or effective enough for crowd control in large public places. To address this limitation, this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm. The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout. In addition to optimizing pedestrian passage efficiency and safety, the paper also considers the situation of pedestrian counterflow, making the guardrail layout more practical. Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion, as well as its significant improvements in passage efficiency and prevention of pedestrian counterflow.

A challenge in crowd simulation is to generate diverse pedestrian motions in virtual environments. Nowadays, there is a greater emphasis on the diversity and authenticity of pedestrian movements in crowd simulation, while most traditional models primarily focus on collision avoidance and motion continuity. Recent studies have enhanced realism through data‐driven approaches that exploit the movement patterns of pedestrians from real data for trajectory prediction. However, they have not taken into account the body‐part motions of pedestrians. Differing from these approaches, we innovatively utilize learning‐based character motion and physics animation to enhance the diversity of pedestrian motions in crowd simulation. The proposed method can provide a promising avenue for more diverse crowds and is realized by a novel framework that deeply integrates motion synthesis and physics animation with crowd simulation. The framework consists of three main components: the learning‐based motion generator, which is responsible for generating diverse character motions; the hybrid simulation, which ensures the physical realism of pedestrian motions; and the velocity‐based interface, which assists in integrating navigation algorithms with the motion generator. Experiments have been conducted to verify the effectiveness of the proposed method in different aspects. The visual results demonstrate the feasibility of our approach. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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 ability to simulate realistic crowds is a highly sought-after capability in the fields of entertainment (video games, movies), urban planning and evacuation simulations. Traditional approaches to crowd simulation rely on heavy Central Processing Unit (CPU) computation. This approach has limitations in terms of scalability and performance, which are solvable with the use of Graphics Programming Units (GPUs) and parallel computing techniques. In fact, the development of Compute Shaders on GPU allows the execution of general-purpose operations alongside traditional rendering tasks within real-time applications. This paper aims to contribute to the current literature on crowd simulation methods by developing a real-time simulation model that integrates and expands several techniques from literature, adapted and optimized to exploit GPU computing capabilities. The proposed model incorporates continuous representations for crowds in order to simulate human movement and decision-making. The achieved results demonstrate a high level of scalability and efficiency. The implemented techniques and optimizations allow the model to handle a significant number of agents while maintaining real-time performances to achieve reduced simulation time and good user experience. Stress tests showcase that the proposed model significantly outperforms other macroscopic models, maintaining a stable frame rate of 60 FPS when simulating 20,000 agents even on mid-range systems intended for personal use.

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.

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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]

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]

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.

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]

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]

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]

Railway station areas can play a crucial role in promoting sustainable development if integrated with cities and be fluctuation-responsive through effective urban design. However, during the design stage, assessing the station areas' performance, of which user satisfaction is indicative, is challenging due to methodological limitations. Agent-based simulation (ABS) is promising as it can link spatial features with agents' behavior features. This research questions to what extent ABS can help assess the urban design of station areas. This paper adopts the user pyramid as the theoretical framework, which outlines five types of user needs: safety, speed, ease, comfort, and experience. The paper selects indicators linking satisfaction and spatial features at the district and building levels. These indicators are measured in the simulation of the station system using digital tools, including MassMotion and Python scripts. The theory, indicators, and tools, in combination, serve as an assessment framework. Rotterdam Central Station is used as a case to demonstrate how the framework works. The framework is capable of assessing design alternatives by identifying changes in user satisfaction. It can be applied on the district level (at a scale of 250 m) with substantial details to inform design decision-making, and it is useful during the design stage when only limited data is available. This paper strengthens the scientific knowledge of railway station areas through the multidisciplinary literature review that translates user needs for urban design use, and it advances the digital means to visualize user satisfaction affected by design. • An assessment framework for evaluating user satisfaction in railway station areas. • Five types of user needs are evaluated: safety, speed, ease, comfort, and experience. • Movement simulation and computer vision are the main methods used. • Rotterdam Central Station is used to show applications of the assessment framework. • Potentially helps design practice and facilitates communication in station development. [ABSTRACT FROM AUTHOR]

Simulating crowd motion in emergency scenarios remains a challenge in computer graphics due to crowd heterogeneity and environmental complexity. However, existing crowd simulation methods homogenize the agent model and simplify target selection and motion navigation of emergency crowds. To address these problems, we propose a multi-agent motion simulation method for emergency scenario deduction. First, we propose a multi-agent model to simulate crowd heterogeneity. This model includes a personality-based heterogeneous agent model and an agent perception model that considers vision, hearing, and familiarity with the environment. Second, we propose a target selection strategy based on the motion patterns of actual pedestrians. This strategy employs mathematical models and our agent perception model to guide agents in selecting appropriate targets. Finally, we propose a global navigation algorithm that combines random sampling with heuristic search methods. Concurrently, we use our multi-agent model to adjust the agent's local motion planning to deduce the motion states of emergency crowds naturally. Experimental results validate that our method can realistically and reasonably simulate crowd motion in emergency scenarios. [ABSTRACT FROM AUTHOR]