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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]

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]

This study introduces a novel framework for choreographing multi-degree of freedom (MDoF) behaviors in large-scale crowd simulations. The framework integrates multi-objective optimization with spatio-temporal ordering to effectively generate and control diverse MDoF crowd behavior states. We propose a set of evaluation criteria for assessing the aesthetic quality of crowd states and employ multi-objective optimization to produce crowd states that meet these criteria. Additionally, we introduce time offset functions and interpolation progress functions to perform complex and diversified behavior state interpolations. Furthermore, we designed a user-centric interaction module that allows for intuitive and flexible adjustments of crowd behavior states through sketching, spline curves, and other interactive means. Qualitative tests and quantitative experiments on the evaluation criteria demonstrate the effectiveness of this method in generating and controlling MDoF behaviors in crowds. Finally, case studies, including real-world applications in the Opening Ceremony of the 2022 Beijing Winter Olympics, validate the practicality and adaptability of this approach. • Advanced MDoF modeling for enhanced crowd behavior simulation. • Combines optimization with spatio-temporal ordering for diverse behaviors. • Introduces innovative time offset and interpolation control functions. • Interactive tools for user-defined crowd behavior customization. • Proven effectiveness in real-world Olympic performance applications. [Display omitted] [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]

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]

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]

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]

• Extreme-density crowds (4+ people per square meter) bear similarities to fluids. • We extend agent-based crowd simulation to extreme densities using Smoothed Particle Hydrodynamics (SPH). • SPH forces augment the usual navigation behavior and contact forces for each agent. • Depending on density, agents blend between collision avoidance and fluid-like interactions. • SPH improves stability, density control, and replication of shockwaves, all in real-time. [Display omitted] In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities. In this paper, we combine agent-based crowd simulation with Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. We integrate SPH into the crowd simulation loop by treating each agent as a fluid particle. The forces of SPH (for pressure and viscosity) then augment the usual navigation behavior and contact forces per agent. We extend the standard SPH model with a dynamic rest density per particle, which intuitively controls the crowd density that an agent is willing to accept. We also present a simple way to let agents blend between individual navigation and fluid-like interactions depending on the SPH density. Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Also, density-based blending between collision avoidance and SPH improves the simulation of mixed-density scenarios. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities. [ABSTRACT FROM AUTHOR]

• We propose an augmented crowd simulation system using automatic determination and reconstruction of navigable areas in static, surveillancealike videos. • We utilize pedestrian trajectory data and use deep learning-based semantic segmentation methods to identify navigable areas. • We simulate artificial agents over the reconstructed navigable area together with real agents in the video via collision avoidance. • We demonstrate the accuracy and applicability of the proposed navigable area reconstruction approach on various crowded outdoor scenarios. [Display omitted] Crowd simulations imitate the group dynamics of individuals in different environments. Applications in entertainment, security, and education require augmenting simulated crowds into videos of real people. In such cases, virtual agents should realistically interact with the environment and the people in the video. One component of this augmentation task is determining the navigable regions in the video. In this work, we utilize semantic segmentation and pedestrian detection to automatically locate and reconstruct the navigable regions of surveillance-like videos. We place the resulting flat mesh into our 3D crowd simulation environment to integrate virtual agents that navigate inside the video avoiding collision with real pedestrians and other virtual agents. We report the performance of our open-source system using real-life surveillance videos, based on the accuracy of the automatically determined navigable regions and camera configuration. We show that our system generates accurate navigable regions for realistic augmented crowd simulations. [ABSTRACT FROM AUTHOR]

Smart City indeed become a vision of most of the countries. The city will have robots, drone, smart car, the house entirely operated Intelligently. However, managing how the human crowd and machine can live together is a challenging task; social and emotional interaction between human may affect the crowd behaviour. Therefore, Crowd simulation has the potential to demonstrate the behaviour of a massive people that gathering on a particular location during a specified period. The size of the crowd, geographical site condition and agent's personality potentially make crowd simulation more believable. This paper aims to observe the potential of reinforcement learning for controlling Socio-emotional crowd behaviour by adding the parameter of emotion towards the crowds. The Tree algorithm more superior compare to other machine learning algorithm that capable of predicting the female agent with accuracy 88.1% and male agent around 85.3%. The simulation performed with the railway station scene that has four platform and six lanes to accommodate a passenger. Simulation is initiated with passengers walking toward the main entrance and went to the desired platform. The train is set up to arrive every minute, and the on-board passenger will move toward the entrance door and exit the central station. The reinforcement learning with socio-emotional control expected to bring the crowd simulation to provide realistic and human mimicked behaviour. [ABSTRACT FROM AUTHOR]

• An agent in a crowd simulation typically uses multiple navigation algorithms. • We convert the output of each navigation algorithm to a topological strategy. • We let an agent systematically detect and resolve conflicts between its strategies. • Thus, we synchronize an agent's algorithms and bridge conceptual gaps between them. • Conflict resolution helps improve the behavior of agents and of the overall crowd. We present a novel topology-driven method for enhancing the navigation behavior of agents in virtual environments and crowds. In agent-based crowd simulations, each agent combines multiple navigation algorithms for path planning, collision avoidance, and more. This may lead to undesired motion whenever the algorithms disagree on how an agent should pass an obstacle or another agent. In this paper, we argue that all navigation algorithms yield a strategy : a set of decisions to pass obstacles and agents along the left or right. We show how to extract such a strategy from a (global) path and from a (local) velocity. Next, we propose a general way for an agent to resolve conflicts between the strategies of its algorithms. For example, an agent may re-plan its global path when collision avoidance suggests a detour. As such, we bridge conceptual gaps between algorithms, and we synchronize their results in a fundamentally new way. Experiments with an example implementation show that our strategy concept can improve the behavior of agents while preserving real-time performance. It can be applied to many agent-based simulations, regardless of their specific navigation algorithms. The concept is also suitable for explicitly sending agents in particular directions, e.g. to simulate signage. [ABSTRACT FROM AUTHOR]

• A machine learning framework to learn the relationship between crowd motion and environments. • An optimization framework which efficiently discovers optimal configurations of environment layouts while considering crowd movement features, without needing to run expensive crowd simulations. • A case study of the Metropolitan Museum of Fine Arts to demonstrate the efficacy of our framework. Evaluating and optimizing the design of built and yet-to-be-built environments, with respect to human occupancy and behavior is both greatly beneficial and challenging. Crowd simulation can provide the computational means to analyze a design through the movement of virtual occupants (agents). A range of analytic information (metrics) can be computed from the simulated movement of the agents that offer insights on the design. Crowd simulation and the related analysis can be part of interactive or offline design optimization pipelines. Unfortunately, large scale crowd simulations are prohibitively expensive, especially when used within iterative design and optimization loops, where hundreds of simulations often need to be computed at interactive rates. We propose a machine learning framework that aims to solve this problem by learning the relationship between a building design and the evaluation metrics extracted from expensive simulations. We train an offline regression neural network using a synthetic training set that we generate for this purpose. Once the network is trained it can evaluate new designs efficiently, and approximate the corresponding analytic information with high accuracy. The proposed framework can also be used to find an optimized layout. We demonstrate the effectiveness of the framework on a variety of real world case studies. [ABSTRACT FROM AUTHOR]

In terrorist attacks, crowd emotions have a significant impact on the evacuation process. Current researches of crowd emotion modeling are of great importance in formulating emergency plans. However, existing crowd emotional models do not describe the perceptual process in detail. They overlook the audiovisual linkage (AVL), and do not demonstrate head-turning behaviors in evacuation simulation. We propose an AVL-based perception model, which can describe head-turning behaviors in response to dynamic stimuli during emotional events. We also propose a model for calculating crowd emotions and provide a method for representing crowd movements. Compared to existing researches, our method not only improves the perception model but also realizes the influence of perception on agents' emotions and movements. The results of comparative experiments show that the proposed method can effectively simulate audiovisual linkage phenomena in crowd evacuation during terrorist attacks. [Display omitted] • Propose AVL-based perception model considering visual, auditory, and the linkage between visual and auditory. • Realize the head-turning behavior of a escapee during terrorist attacks. • Propose emotion calculation model based on Durupinar theory. Combine with enhanced driving force model to show perception's impact on emotion and movement. • Validate security broadcasts' efficacy with proposed method. Explore the effect of location and number of security broadcasts on evacuation efficiency. [ABSTRACT FROM AUTHOR]

• Improved the value and judgment conditions of the psychological force. • The modified social force model eliminates the abnormal phenomenon of evacuees' speed oscillation near fixed obstacles that exists in the original model. • The effectiveness of the modified social force model was validated by comparing the simulation results with the actual experimental results. The social force model is one of the most common models used in the study of pedestrian dynamics. In the social force model, there are oscillation in the movement of pedestrians. To address the oscillation phenomenon, this paper modifies the way of setting psychological forces and judgment conditions based on previous research. By comparing the simulation results with the real experimental results, the evacuation time results are basically the same, which validates the effectiveness of the modified social force model. The simulation results show that the modified social force model eliminates the phenomenon of evacuee velocity oscillations near fixed obstacles in the original model and fixes the phenomenon of abnormal behavior of evacuees during collisions. In addition, we simulate different numbers of evacuees (i.e.,25,50,100,200) and the results show that the modified social force model successfully replicates the phenomenon of arch congestion during evacuation. [ABSTRACT FROM AUTHOR]

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

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

Highlights • We show how crowds can be simulated within the PBD framework by augmenting it with agent-based planning velocities. • We adopt the position-based frictional contact constraints of granular materials to model local collision avoidance. • XSPH viscosity approximates collective group behavior. Our model permits the natural development of agent groups. • We develop a novel long-range collision avoidance constraint to deal with anticipatory collisions. • We demonstrate "multi-species" crowd coupling by supporting spatially varying Lagrangian physical properties. Graphical abstract Abstract We introduce a crowd simulation method that runs at interactive rates for on the order of a hundred thousand agents, making it particularly suitable for use in games. Our new method is inspired by Position-Based Dynamics (PBD), a fast physics-based animation technique in widespread use. Individual agents in crowds are abstracted by particles, whose motions are controlled by intuitive position constraints and planning velocities, which can be readily integrated into a standard PBD solver, and agent positions are projected onto constraint manifolds to eliminate colliding configurations. A variety of constraints are presented, enabling complex collective behaviors with robust agent collision avoidance in both sparse and dense crowd scenarios. [ABSTRACT FROM AUTHOR]

Abstract In large crowd events, there is always a potential possibility that a stampede accident will occur. The accident may cause injuries or even death. Approaches for controlling crowd flows and predicting dangerous congestion spots would be a boon to on-site authorities to manage the crowd and to prevent fatal accidents. One of the most popular approaches is real-time crowd simulation based on position data from personal Global Positioning System (GPS) devices. However, the accuracy of spatial data varies for different GPS devices, and it is also affected by an environment in which an event takes place. In this paper, we would like to assess the effect of position errors on stampede prediction. We propose an Automatic Real-time dEtection of Stampedes (ARES) method to predict stampedes for large events. We implement three different stampede assessment methods in Menge framework and incorporate position errors. Our analysis suggests that the probability of simulated stampede changes significantly with the increase of the magnitude of position errors, which cannot be eliminated entirely with the help of classic techniques, such as the Kalman filter. Thus, it is our position that novel stampede assessment methods should be developed, focusing on the detection of position noise and the elimination of its effect. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of efficiently simulating a believable virtual crowd. Our method is the first one that uses the Neural Network (NN) model to fit behaviors from real crowd data to a crowd simulation. Unlike several rule-based approaches that often result in ‘walking robots’, our model can learn motion rules derived from real data and later simulate human walking motions. Additionally, unlike the existing data-driven crowd simulation methods that have to perform search operations on the bound dataset simultaneously during the simulation, our model directly uses the NN model to generate the proper motion for each crowd member. The proposed method is being tested on various scenarios and compared with state-of-the-art state-action-based methods that are commonly employed in data-driven crowd simulation systems. The results demonstrate a significant increase in speed, as well as better simulation quality. [ABSTRACT FROM AUTHOR]

Abstract Simulating large and complex urban environments for real-time simulation poses multiple challenges which are not trivial to solve, for instance, gathering information of geometry for cities to be displayed, management of large quantities of data and fetching efficiently the information needed for the simulation and visualization of virtual pedestrians are just some examples of the challenges we face. In this work we present the results we have achieved so far using a system that addresses problems such as data visualization using level of detail techniques, GPU processing for efficient computation and the integration of real data into the simulation such as GPS data and real 3D cities that are generated using tools such as WRLD-3D plug-in. This method has been proven powerful enough to handle large crowds composed by thousands of agents in urban environments at interactive frame rates (at least 30 frames per second or 33.33 ms per frame) without compromising the visual quality and the complexity of the behaviors by using publicly real world data such as city topography and pedestrian GPS data. [ABSTRACT FROM AUTHOR]

Abstract Visualization of human flows and crowd behavior is a complex problem of multi-agent modeling. It can be applied to a various set of problems, from emergency case planning to city life visualization. In this paper, we propose a system to build composite behavior structures for models with a big number of agents. It is designed with combining game development technologies of creating artificial intelligence for ambient characters and traditional multi-agent modeling methods. The system was applied to simulate and to visualize human flows on Sochi Olympic Park station during 2014 Winter Olympics and nowadays in order to reveal areas where pedestrian flow should be controlled. [ABSTRACT FROM AUTHOR]

We propose a data-driven approach for tuning, validating and optimizing crowd simulations by learning parameters from real-life videos. We discuss the common traits of incidents and their video footages suitable for the learning step. We then demonstrate the learning process in three real-life incidents: a bombing attack, a panic situation on the subway and a Black Friday rush. We reanimate the incidents using an existing emotion contagion and crowd simulation framework and optimize the parameters that characterize agent behavior with respect to the data extracted from the video footages of the incidents. [ABSTRACT FROM AUTHOR]

We present an online parameter learning algorithm for data-driven crowd simulation and crowd content generation. Our formulation is based on incrementally learning pedestrian motion models and behaviors from crowd videos. We combine the learned crowd-simulation model with an online tracker to compute accurate, smooth pedestrian trajectories. We refine the motion model using an optimization technique to estimate the agents׳ simulation parameters. We also use an adaptive-particle filtering scheme for improved computational efficiency. We highlight the benefits of our approach for improved data-driven crowd simulation, including crowd replication, augmented crowds and merging the behavior of pedestrians from multiple videos. We highlight our algorithm׳s performance in various test scenarios containing tens of human-like agents and evaluate it using standard metrics. [ABSTRACT FROM AUTHOR]

Traditionally, the ship cabin layout is designed by experts with the help of CAD, GAD, etc. The layout is usually drawn and designed repeatedly, which is not only labor-intensive but also time-consuming. Although a layout is well-thought-out, potential design issues may still exist in practice. In recent years, some intelligent ship layout design algorithms are proposed. However, the capability of involving humans in the loop has been seriously overlooked in current studies, which are mostly end-to-end approaches. To resolve these challenges, we propose a novel interactive ship cabin layout optimization approach based on integer programming (IP), which is capable to generate diverse layouts and interact with designers for further refinement. Gurobi, an integer programming solver, is adopted to find out the optimal value under the layout constraints in our approach. Moreover, crowd simulation is used to evaluate the layout quality from the perspective of coziness and mobility. The results indicate the proposed method can effectively design and optimize the ship cabin layout in an interactive manner. • This paper proposes an interactive optimization approach for the ship cabin layout design. • Our method assists designers to complete repetitive and complex operations automatically. • The mobility and coziness metrics of crowd simulation are introduced to evaluate the ship cabin layouts. • Designers can define the passages in arbitrary shapes, breaking the limitation of predefined passage patterns. [ABSTRACT FROM AUTHOR]

In the paper we present a modern efficient parallel OpenMP+CUDA implementation of crowd simulation for hybrid CPU+GPU systems and demonstrate its higher performance over CPU-only and GPU-only implementations for several problem sizes including 10 000, 50 000, 100 000, 500 000 and 1 000 000 agents. We show how performance varies for various tile sizes and what CPU–GPU load balancing settings shall be preferred for various domain sizes among CPUs and GPUs of a high performance system with 2 Intel Xeon Silver multicore CPUs and 8 NVIDIA Quadro RTX 5000 GPUs. We then present how execution time depends on the number of agents as well as the number of CUDA streams used for parallel execution of several CUDA kernels. We discuss the design and implementation of an algorithm with CPU computational threads, GPU management threads, assignment of particular tasks to threads as well as usage of pinned memory and CUDA shared memory for maximizing performance. [ABSTRACT FROM AUTHOR]

This paper presents a formation control system that can be applied in many areas under defined conditions. The system allows users to simplify interaction and quickly complete formation arrangements in virtual scenarios. At the same time, the system allows the user to input specific formations to meet requirements, such as war formations in video games or formation arrangements for stage performances. It also recognizes user input through a human–computer interaction method based on gesture recognition and finds the best corresponding formation from a library of predefined and user-defined formations. The system ensures real-time user interaction and control by automatically scaling the group size and controlling the group movement according to the unit density. During formation changes, the minimum energy principle is applied to determine the ideal position of each agent in the target formation to increase the efficiency of its movement. In addition, formation stability is maintained during collision avoidance by combining potential obstacle fields with a rigid formation network. Finally, the proposed formation system is tested qualitatively and compared quantitatively with the execution efficiency of some existing interactive formation methods. The experimental results show that the method can effectively implement formation control in a virtual scene. It can automatically avoid obstacles and maintain the formation during the formation morphing process. [ABSTRACT FROM AUTHOR]

Most of the existing approaches to simulate heterogeneous crowd behaviors focus on the aspect of psychology. From a human׳s physiological characteristics perspective, this paper presents a method to generate heterogeneous crowd behaviors. We choose four basic physiological characteristics, including gender , age , health and body shape , and map them to a navigation method, which is reciprocal velocity obstacle approach in the paper. The mapping parameters are determined through a two-step process. Firstly, a video based method is proposed to obtain simulation parameters for single physiological feature by tracking and analyzing trajectories of persons in real video scenes, and then a comprehensive mapping is presented to combine all characteristics together to generate parameters for a certain person. Through a number of simulations and validation experiments, we demonstrate that the proposed method is simple but effective and efficient to exhibit heterogeneous behaviors of crowds. [ABSTRACT FROM AUTHOR]

Pedestrian and crowd models have been on a rise over the past few decades which has been marked by emergence of a multitude of approaches and their implementations. However, determining and proving whether particular models reproduce the intended process adequately is still a complex task. Not only do researchers aim to assure that proposed algorithms correlate with the real-world mechanisms of collective behavior, but seek ways to adjust the crucial parameters of the model that vary between the cases to the observations and experimental results. Following this line of thought, we have calibrated our pedestrian simulator through rather frugal optimization of parameters with genetic algorithms to the data derived from the analysis of the real-world reference data. We present the results of such optimization along with the description of the case studied and the overall process of data collection. The paper is concluded with a discussion of the results of the calibration, observations made throughout the research and perspectives for further studies. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematical model and a computational approach applied to the study of the interaction between a moving platform and pedestrians walking on it. A ship deck motion used as a basic scenario for simulation is realized in three modes reproducing heaving, pitching or rolling rotations of the vessel. Behavior of virtual passengers is emulated by the Social Force pedestrian model modified with additional forces. In this paper the mathematical definitions of both models and their software implementations are discussed. The results of the experiments reproducing various combinations of characteristics of the ship motion are presented and compared with a case where it remained stationary. The paper is concluded with an analysis of the simulation results and perspectives for further research. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel technique termed miSFM for the simulation of crowd evacuation. miSFM take merits of both Mutual Information (MI) and Social Force Model (SFM). More specifically, MI of interacting agents is adopted to determine the level of order within a crowd during an evacuation. In such a way, SFM can be improved by adapting the forces involved at microscopic level between mutually interacting agents. The key innovation lies in highlighting how the dynamic adjustment of SFM parameters reveals much more realistic crowd movements for the evacuation simulation. Extensive experiments over several alternative and state-of-the-art works demonstrate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this work the framework for detectors layout optimization based on a multi-agent simulation is proposed. Its main intention is to provide a decision support team with a tool for automatic design of social threat detection systems for public crowded places. Containing a number of distributed detectors, this system performs detection and an identification of threat carriers. The generic model of detector used in the framework allows to consider detection of various types of threats, e.g. infections, explosives, drugs, radiation. The underlying agent-based models provide data on social mobility, which is used along with a probability based quality assessment model within the optimization process. The implemented multi-criteria optimization scheme is based on a genetic algorithm. For experimental study the framework has been applied in order to get the optimal detectors’ layout in Pulkovo airport. [ABSTRACT FROM AUTHOR]

This paper presents a real-time animation system for fully embodied virtual humans that satisfies accurate foot placement constraints for different human walking and running styles. Our method offers a fine balance between motion fidelity and character control, and can efficiently animate over sixty agents in real time (25 FPS) and over a hundred characters at 13 FPS. Given a point cloud of reachable support foot configurations extracted from the set of available animation clips, we compute the Delaunay triangulation. At runtime, the triangulation is queried to obtain the simplex containing the next footstep, which is used to compute the barycentric blending weights of the animation clips. Our method synthesizes animations to accurately follow footsteps, and a simple IK solver adjusts small offsets, foot orientation, and handles uneven terrain. To incorporate root velocity fidelity, the method is further extended to include the parametric space of root movement and combine it with footstep based interpolation. The presented method is evaluated on a variety of test cases and error measurements are calculated to offer a quantitative analysis of the results achieved. [ABSTRACT FROM AUTHOR]

The authors propose a new methodology for creating realistic and effective models of crowd dynamics, which takes into account the Agent-based approach combined with non-homogeneous and asynchronous Cellular Automata. The proposed methodology makes it possible to model pedestrians׳ dynamics in complex environments, like stadiums or shopping centers, and enables mimicking of the pedestrians׳ complex decision making process on different levels: strategic and tactical/operational. On the other hand, the use of the Agent-based approach makes it possible to apply different scenarios and situational contexts, namely competitive and non-competitive evacuation or free movement of pedestrians. The proposed approach was tested in large-scale test cases, namely the evacuation of the Allianz Arena football stadium in Munich and other stadiums like Wisla Krakow Stadium or GKS Tychy Stadium, as well as AGH University facilities. [ABSTRACT FROM AUTHOR]

Abstract: Multiagent-based simulations enable us to validate different use-case scenarios in a lot of application domains. The idea is to develop a realistic virtual environment to test particular domain-specific procedures. This paper presents a multilevel model of a physic environment for the simulation of crowd in a virtual 3D building. The major contributions of this paper is the agentification of the model to support multilevel simulation of the environment. Finally, the application of the model inside an airport terminal is presented. [Copyright &y& Elsevier]

Abstract: The Love Parade disaster in Duisberg, Germany lead to several deaths and injuries. Disasters like this occur due to the existence of high densities in a limited area. We propose a wearable electronic device that helps reduce such disasters by directing people and thus controlling the density of the crowd. We investigate the design and effectiveness of such a device through an agent based simulation using social force. We also investigate the effect of device failure and participants not paying attention in order to determine the critical number of devices and attentive participants required for the device to be effective. [Copyright &y& Elsevier]

Abstract: Formation control technology can exhibit the collective flock behaviors of a crowd for simulation and animation purpose, and thus, can be applied in various fields. In this paper, an innovative geometry-constrained framework for smooth formation animation of regulated crowds is proposed. We employ the morphing method to generate a series of in-between constrained shapes as key frames to impose process control and ensure smoothness of formation transformations. We also introduce centroidal Voronoi tessellation (CVT) to calculate optimal distribution of agents, and present an improved Lloyd descent method to perform path planning by utilizing its fixed point iteration feature. As extensions, the proposed framework can handle environmental obstacles avoiding problems for the whole crowd to preserve certain formation extremely by utilizing a domain modification method, and can also be adapted to 3D spaces and density-based domains. Experimental results show that the proposed method can generate stable, smooth, orderly, regular and elegant crowd formation animations. [Copyright &y& Elsevier]

Abstract: As crowd simulation in micro-spatial environment is more widely applied in urban planning and management, the construction of an appropriate spatial data model that supports such applications becomes essential. To address the requirements necessary to building a model of crowd simulation and people–place relationship analysis in micro-spatial environments, the concept of the grid as a basic unit of people–place data association is presented in this article. Subsequently, a grid-based spatial data model is developed for modelling spatial data using Geographic Information System (GIS). The application of the model for crowd simulations in indoor and outdoor spatial environments is described. There are four advantages of this model: first, both the geometrical characteristics of geographic entities and behaviour characteristics of individuals within micro-spatial environments are involved; second, the object-oriented model and spatial topological relationships are fused; third, the integrated expression of indoor and outdoor environments can be realised; and fourth, crowd simulation models, such as Multi-agent System (MAS) and Cellular Automata (CA), can be further fused for intelligent simulation and the analysis of individual behaviours. Lastly, this article presents an experimental implementation of the data model, individual behaviours are simulated and analysed to illustrate the potential of the proposed model. [Copyright &y& Elsevier]

Abstract: This article introduces a new collision avoidance model enabling the design of efficient realistic virtual pedestrian and cyclist behaviors. It is a force-based model using collision prediction with dynamic time-windows to predict future potential collisions with obstacles and other individuals. It introduces a new type of force called sliding force to allow a smooth avoidance of potential collisions while enabling the pedestrian to continue to progress towards its goal. Unlike most existing models, our forces are not scaled according to the distance to the obstacle but depending on the estimate of the collision time with this obstacle. This inherently integrates obstacles’ velocity. This greatly reduces the compu- tational complexity of the model while ensuring a smooth avoidance. This model is oscillation-free except for concave obstacles. It enables the reproduction of inherent emergent properties of real crowds such as spontaneous organizations of pedestrians into lane lines, etc. This model is computationally efficient and designed for real time simulation of large crowds. [Copyright &y& Elsevier]

Abstract: We present a new way of drawing a crowd of animated characters in real-time. Previous work has focused almost exclusively on how to visualize ever larger crowd scenes and the current state-of-the-art can display tens of thousands of virtual humans with ease. The associated trade-off, however, is that crowd members can do little more than play a set of scripted motion clips. It follows that designating individuals to be members of a crowd instantly limits the techniques that can be used, the behaviours that can be depicted and ultimately, the perceived realism of a scene. Our approach differs from the state-of-the-art in that we do not propose a crowd-specific technique but instead a bone-parallel, OpenCL-accelerated interpretation of the traditional character pipeline. The method does not rely on pre-processing; provides fine-grained control over the animation of a crowd (support for motion blending and varied skeletons, for example) and crowd members and user-controlled ‘hero’ characters can be handled without distinction. [Copyright &y& Elsevier]

Abstract: A crowd behavior model based on reciprocal velocity obstacle algorithm is proposed in this paper. The model is the combination autonomous navigation and crowd behavior strategy module. Reciprocal Velocity Obstacle algorithm is adopted to achieve global path planning. individuals act quite differently under various circumstances, and several behavior strategies are applied in crowd simulation on the basis of social theory: in normal condition people show their humility confronting interaction, therefore they queue up with the application of stop rules; while in emergency circumstances, competition feature appears during inter-individual interactive process, as a result they decelerate towards their goals with the arching and congestion phenomena observed. The results show the good performance for crowd evacuation in real time and realism of the simulation process. [Copyright &y& Elsevier]

Abstract: This paper presents a method for crowd simulation based on a biologically motivated space colonization algorithm. This algorithm was originally introduced to model leaf venation patterns and the branching architecture of trees. It operates by simulating the competition for space between growing veins or branches. Adapted to crowd modeling, the space colonization algorithm focuses on the competition for space among moving agents. Several behaviors observed in real crowds, including collision avoidance, relationship of crowd density and speed of agents, and the formation of lanes in which people follow each other, are emergent properties of the algorithm. The proposed crowd modeling method is free-of-collision, simple to implement, robust, computationally efficient, and suited to the interactive control of simulated crowds. [Copyright &y& Elsevier]

Abstract: Soccer has always been one of the most popular videogame genres. When designing a soccer game, designers tend to focus on the game field and game play due to the limited computational resources, and thus the modelling of virtual spectators is paid less attention. In this study we present a novel approach to the modeling of spectator behavior, which treats each spectator as a unique individual. We also propose an independent software layer for sport-based games that simply obtains the game status from the game engine via a simple messaging protocol and computes the spectator behavior accordingly. The result is returned to the game engine, to be used in the animation and rendering of the spectators. Additionally, we offer a customizable spectator knowledge base with well structured XML to minimize coding efforts, while generating individualized behavior. The employed AI is based on fuzzy inference. In order to overcome additional demand for computing realistic spectator behavior, we use GPU parallel computing with CUDA. [Copyright &y& Elsevier]

Abstract: Masjid Al-Haram in Saudi Arabia is one of the most crowded pilgrimage locations in the world. More than two million pilgrims gather in Saudi Arabia annually during the Hajj season, and it is compulsory for them to perform a series of actions in the mosque. In the court area, pilgrims perform one of the most important rituals of Hajj, called Tawaf, which consists of seven laps of circular movement around the Kaabah, which is situated in the centre of the court. After the Tawaf, pilgrims pray in the court and leave from one of several doors. In this paper, we present a cellular automata model for the simulation of the pilgrims’ circular Tawaf movement. We also use a discrete-event model to simulate the actions and behaviours of the pilgrims. The proposed models are used in a software platform to simulate the actions and movements of pilgrims in the area. We then present an example application of the model in predicting whether specific changes to the architecture could increase the throughput of the system. [Copyright &y& Elsevier]

Abstract: This paper presents a novel approach for crowd simulation in complex environments. Our method is based on the continuum model proposed by Treuille et al. [13]. Compared to the original method, our solution is well-suited for complex environments. First, we present an environmental structure and a corresponding discretization scheme that helps us to organize and simulate crowds in large-scale scenarios. Second, additional discomfort zones around obstacles are auto-generated to keep a certain, psychologically plausible distance between pedestrians and obstacles, making it easier to obtain smoother trajectories when people move around these obstacles. Third, we propose a technique for density conversion; the density field is dynamically affected by each individual so that it can be adapted to different grid resolutions. The experiment results demonstrate that our hybrid solution can perform plausible crowd flow simulations in complex dynamic environments. [ABSTRACT FROM AUTHOR]

Abstract: Macroscopic and microscopic modeling have become mainstream methodologies for crowd simulation in dynamic environments. The two models make a trade-off between efficiency and accuracy, but neither of them is able to achieve both goals at the same time. With the aim of achieving both efficiency and accuracy, a hybrid modelling method is proposed in this paper for crowd simulation. This paper illustrates how the two types of models co-exist in a single simulation and work collaboratively. A case study for this method is also conducted, the simulation result of which shows that the proposed method can not only benefit from the macroscopic model by improving the simulation efficiency, but also obtain a fine-grained simulation result by adopting the microscopic model. [ABSTRACT FROM AUTHOR]