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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In crowd evacuation simulation, a number of exit point and obstacles play an important role that can influence the result in the evacuation simulation. This paper focuses on the movement of the crowd’s emergency evacuation based on a modified social force model (SFM) via optimising the obstacles interaction parameter in one the SFM component. The simulation also compared original SFM (without obstacles) and modified SFM (with obstacles). The results show the impact can minimize the concept of arching phenomenon (faster-is-slower). For an obstacles issue, it is proven that obstacles can help to reduce evacuation time in regards to its proper position and exit width.

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

At present, environment designers mostly use their intuition and experience to predictively account for how environments might support dynamic activity. The majority of Computer‐Aided Design tools only provide a static representation of space which potentially ignores the impact that an environment layout produces on its occupants and their movements. To address this, computational techniques such as crowd simulation have been developed. With few exceptions, crowd simulation frameworks are often decoupled from environment modeling tools. They usually require specific hardware/software infrastructures and expertise to be used, hindering the designers' abilities to seamlessly simulate, analyze, and incorporate movement‐centric dynamics into their design workflows. To bridge this disconnect, we devise a cross‐browser service‐based simulation analytics platform to analyze environment layouts with respect to occupancy and activity. Our platform allows users to access simulation services by uploading three‐dimensional environment models in numerous common formats, devise targeted simulation scenarios, run simulations, and instantly generate crowd‐based analytics for their designs. We conducted a case study to showcase cross‐domain applicability of our service‐based platform, and a user study to evaluate the usability of this approach. [ABSTRACT FROM AUTHOR]

Increasing attention is being given to the modeling and simulation of traffic flow and crowd movement, two phenomena that both deal with interactions between pedestrians and cars in many situations. In particular, crowd simulation is important for understanding mobility and transportation patterns. In this paper, we propose an emotion-based crowd simulation model integrating physical strength consumption. Inspired by the theory of “the devoted actor,” the movements of each individual in our model are determined by modeling the influence of physical strength consumption and the emotion of panic. In particular, human physical strength consumption is computed using a physics-based numerical method. Inspired by the James-Lange theory, panic levels are estimated by means of an enhanced emotional contagion model that leverages the inherent relationship between physical strength consumption and panic. To the best of our knowledge, our model is the first method integrating physical strength consumption into an emotion-based crowd simulation model by exploiting the relationship between physical strength consumption and emotion. We highlight the performance on different scenarios and compare the resulting behaviors with real-world video sequences. Our approach can reliably predict changes in physical strength consumption and panic levels of individuals in an emergency situation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In emergency situation, mass panic often causes more causalities than the disaster itself. The crowd emotional model could be used to simulate how crowd behavior in emergency scenarios and be helpful for developing crowd evacuation plans in emergency situations. However, existing crowd emotional models usually set model parameters in an empirical manner and are not validated by real cases. In this paper, a crowd emotional model is proposed to simulate the crowd movement in outdoor emergency situations. First of all, the crowd entropy and the movement difference are proposed to describe the emotional impact of the crowd scene on the agents. The perception of vision and hearing are considered, and the calculation formulas of the agent's emotional intensity and crowd emotional contagion are proposed. By calculating individual trajectories in the real video, the cumulative differences between the movements of the real crowd and the corresponding virtual crowd are analyzed. At last, a multi‐parameter optimization method is implemented by the differential evolution algorithm. To verify the parameters in models, three videos which are generated from three real cases, including explosion attack, shooting incident, and crowd disturbance are selected for experimental verification. The results showed that the proposed model could be a feasible method for optimizing parameters to simulate the emergency scenario. [ABSTRACT FROM AUTHOR]

For tracking systems consisting of multiple cameras with overlapping field-of-views, homography-based approaches are widely adopted to significantly reduce occlusions among pedestrians by sharing information among multiple views. However, in these approaches, the usage of information under real-world coordinates is only at a preliminary level. Therefore, in this paper, a multi-camera tracking system with integrated crowd simulation is proposed in order to explore the possibility to make homography information more helpful. Two crowd simulators with different simulation strategies are used to investigate the influence of the simulation strategy on the final tracking performance. The performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) metrics, for all the camera views and the results obtained under real-world coordinates. The experimental results demonstrate that crowd simulators boost the tracking performance significantly, especially for crowded scenes with higher density. In addition, a more realistic simulation strategy helps to further improve the overall tracking result.

For tracking systems consisting of multiple cameras with overlapping field-of-views, homography-based approaches are widely adopted to significantly reduce occlusions among pedestrians by sharing information among multiple views. However, in these approaches, the usage of information under real-world coordinates is only at a preliminary level. Therefore, in this paper, a multi-camera tracking system with integrated crowd simulation is proposed in order to explore the possibility to make homography information more helpful. Two crowd simulators with different simulation strategies are used to investigate the influence of the simulation strategy on the final tracking performance. The performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) metrics, for all the camera views and the results obtained under real-world coordinates. The experimental results demonstrate that crowd simulators boost the tracking performance significantly, especially for crowded scenes with higher density. In addition, a more realistic simulation strategy helps to further improve the overall tracking result.

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

• 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]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)