Search

Although urban digital twins are still at an embryonic stage of development, their use cases are multiple, ranging from big data aggregation to simulations. Additionally, predictions can be rendered and quickly implemented using actuators to transform physical environments and influence urban life. In this article, we investigate the potential of an agent-based model in a smart city setting to predict emergent behavior in relation to the suppression of civil violence by implementing crowd management practices. To this end, we designed a simulation environment that includes cameras in public spaces and wearable sensors, and considers nudging and self-nudging processes supported by a surveillance apparatus. Building on Epstein’s threshold-based model of civil violence, the proposed simulation is informed by surveillance theories and contemplates methods for crowd monitoring and social control. The experiments’ results provide insights into how specific measures and combined actions may influence the suppression of civil violence in public spaces and can be useful to inform crowd management activities and policymaking. Moreover, we use the simulation to reflect upon the potentials and limitations of integrating agent-based models into urban digital twins and emphasize the imminent risks for individuals and democratic societies of employing a ubiquitous surveillance apparatus endowed with the autonomy to trigger actuators.

We introduce view birdification, the problem of recovering ground-plane movements of people in a crowd from an ego-centric video captured from an observer (e.g., a person or a vehicle) also moving in the crowd. Recovered ground-plane movements would provide a sound basis for situational understanding and benefit downstream applications in computer vision and robotics. In this paper, we formulate view birdification as a geometric trajectory reconstruction problem and derive a cascaded optimization method from a Bayesian perspective. The method first estimates the observer's movement and then localizes surrounding pedestrians for each frame while taking into account the local interactions between them. We introduce three datasets by leveraging synthetic and real trajectories of people in crowds and evaluate the effectiveness of our method. The results demonstrate the accuracy of our method and set the ground for further studies of view birdification as an important but challenging visual understanding problem. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature 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 the present study, we focus on modeling and simulations of macroscopic behavior of a crowd of people under emergency conditions. We present a newly developed integrated numerical approach, which combines the time-dependent RANS method in predicting the temporal and spatial evolution of a heavy gas turbulent dispersion with the macroscopic model of a crowd movement. The coupling is imposed through locally determined concentration of the heavy gas which directly impacts the movement of the crowd of people. The potential of the developed approach is demonstrated in a series of cases that include the crowd behavior on an open railway platform and inside a train station, as well as predictions of field studies of turbulent dispersion of a heavy gas under windy conditions. Finally, various scenarios have been analyzed of the coupling between the local concentration of a potentially harmful heavy gas on crowd behavior through different forms of the cost functions. In conclusion, the version of the integrated model is recommended for future optimization studies of crowd dynamics following an incidental release of heavy gas. [ABSTRACT FROM AUTHOR]

• Estimation of the contact forces generated in a dense crowd from both microscopic and macroscopic points of view. • Rigorously define and model contact forces through the non-smooth contact dynamics theory. • The choice of the pedestrian's shape influences the contact forces in the microscopic representation. • Model the crowd as a deformable solid and calculate the contact forces using the stress tensor. • Critical study and overview on contact force calculation techniques and comparison with the developed approaches. This paper deals with the estimation of pressure at collisions times during the movement of a dense crowd. Through the non-smooth contact dynamics approach for rigid and deformable solids, proposed by Frémond and his collaborators, the value of pressure and contact forces at collisions points, generated through congestion or panic situation are estimated. Firstly, we propose a second-order microscopic model, in which the crowd is treated as a system of rigid solids. Contact forces are rigorously defined by taking into account multiple simultaneous contacts and the non-overlapping condition between pedestrians. We show that for a dense crowd, percussions can be seen as contact forces. Secondly, in order to overcome the restrictive hypothesis related to the geometric form adapted to model the pedestrian, a continuous equivalent approach is proposed where the crowd is modeled as a deformable solid, the pressure is then defined by the divergence of the stress tensor and calculated according to volume and surface constraints. This approach makes it possible to retain an admissible right-velocity, including both the non-local interactions between non-neighbor pedestrians and the choice of displacement strategy of each pedestrian. Finally, the comparison between the two proposed approaches and some other existing approaches are presented on several illustrative examples to estimate the contact forces between pedestrians. [ABSTRACT FROM AUTHOR]

Crowds of people are often found in enclosed or constricted spaces. Evacuation in such situations is usually conducted calmly, but real or perceived danger may trigger panic. In panicked crowds, the interpersonal distance crowd members normally observe is often overwhelmed by the physical pressure of crowd members pushing against each other. That pressure can both slow evacuation and lead to injury or death. Models have been developed to study crowd evacuation in a range of situations. This paper describes the implementation, testing, and validation of a crowd evacuation model using Unity, a commercial computer game engine. A realistic physics-based model of crowd movement that calculates and considers the physical pressure crowd members exert on each other was implemented in Unity. The implemented model was tested under both nonpanicked and panicked scenarios; those tests exhibited known qualitative characteristics of such scenarios. The model was then quantitatively validated by comparing its results to the outcome of an actual evacuation event, the 2003 fire at The Station nightclub in Rhode Island. The implementation and validation show that Unity can be an effective tool for evacuation modeling. [ABSTRACT FROM AUTHOR]

With more and more people attending public gatherings, and with a continuous rise in crowd density in urban areas, the management of crowd has become more challenging than ever before. Every year, many people lose their lives due to inefficient crowd planning and management. Crowd management is an interdisciplinary area, and it requires understanding of engineering and technological aspects, along with an understanding of crowd behavior and crowd flow management, i.e. psychological and sociological aspects. This paper presents a broad, but not exhaustive overview of the recent technological advancements in the area of crowd planning and monitoring techniques for an effective crowd management system. It discusses the crowd modeling aspects during the planning of crowded scenario, and the technological advancements in crowd data acquisition techniques [based on Vision, Wireless/Radio-Frequency (RF) and Web/Social-media data mining technologies] during execution of crowded event. The paper also considers technological applications in some highly crowded scenarios on earth as case studies, along with future research directions in the area. [ABSTRACT FROM AUTHOR]

On the basis of data-driven methodology, especially velocity correlations of pedestrians moving in a crowd, we have proposed a new model of pedestrian dynamics with an easy-adjustable space discretization. The model is based on Cellular Automata (CA) with an adaptive lattice and takes into account proxemics patterns among pedestrians. The proposed model uses agents located on the CA lattice, constructed using the concept of Floor Field (FF), namely, a set of gradient potential fields influencing the movement of agents. In the model, we have proposed three kinds of such fields: Static FF—responsible for navigation to agents' Points of Interest (POIs), Wall FF—a repulsive influence with obstacles, and Interplay FF—to model agents' volume and proxemics effects. The third field, which models mutual relations between pedestrians taking into account the rules of proxemics is an added value in the area of crowd modeling and simulation. During the creation of the proposed model, we have based on a set of experimental data provided by Jülich Supercomputing Centre and our previous analysis of spatial patterns and velocity correlations. Thus, the proposed model includes, among other things the most probable positions of the nearest neighbors in a crowd—including the shape of "the forbidden zone" around a pedestrian and the finding of rules explaining observed mutual spatial relations between neighbors in a crowd. In the described model the cell size is an adjustable parameter, with available values from 1 cm to 50 cm. It is possible with the usage of embodied agents which always occupy only one cell of Cellular Automata, actual pedestrian volume is modeled with the Interplay FF. An interesting property of the model is that changing the cell size does not involve changing the rules of the model, e.g. its transition function. • Spatial patterns among pedestrians during evacuation are analyzed. • A new model of pedestrian dynamics with an easy-adjustable space discretization is proposed. • Interplay Floor Field is used to model spatial patterns among pedestrians. [ABSTRACT FROM AUTHOR]

Models of crowd dynamics are critically important for urban planning and management. They support analysis, facilitate qualitative and quantitative predictions, and synthesize behaviors for simulations. One promising approach to crowd modeling relies on micro-level agent-based simulations, where the interactions of simulated individual agents in the crowd result in macro-level crowd dynamics which are the object of study. This article reports on an agent-based model of urban pedestrian crowds, where culture is explicitly modeled. We extend an established agent-based social agent model, inspired by social psychology, to account for individual cultural attributes discussed in social science literature. We then embed the model in a simulation of pedestrians and explore the resulting macro-level crowd behaviors, such as pedestrian flow, lane changes rate, and so on. We validate the model by quantitatively comparing the simulation results to the pedestrian dynamics in movies of human crowds in five different countries: Iraq, Israel, England, Canada, and France. We conclude that the model can faithfully replicate urban pedestrians in different cultures. Encouraged by these results, we explore simulations of mixed-culture pedestrian crowds. [ABSTRACT FROM AUTHOR]

Crowd simulation is one of the most widely used technique for the design and evaluation of the human-in-theloop situation such as evacuation plans, the building designs and so on, in a virtual environment. In order to have a valid evaluation, it is necessary to have a correct model of an individual agent's decision process which causes a behavior of human's in a crowd. However, in general, designing a decision process of agent's largely depends on a trial-and-error manner. To avoid the trial-and-error by human designers, we focus on the automated method to derive agent's decision strategy from the real data of human's. In this paper, we consider a decision process consists of two stages. One is the strategy phase to select a goal state, and the other is the policy phase to output the primitive action of agent's. We focus on the strategy phase. Though it should be more natural to assume that the strategy of each agent is not all the same, the existing method assumes that all agents have a common and homogeneous strategy. The proposed method makes it possible to extract the individual and different strategies of agent's by evolutionary computation. The results of the experiments show the validity of our method. In addition, it is shown that there exist the cases where multiple strategies will be extracted for a single trajectory. [ABSTRACT FROM AUTHOR]

Pedestrian path forecasting is one of the recently emerging applications in visual crowd analysis and modeling. Moreover, of the attempts proposed to date, only a few have considered that the undergoing interaction among agents is a key factor in determining their walking trends in a given scene. To this end, we propose a simple yet efficient framework for pedestrian path prediction in crowded scenes. First, we extract motion features related to the target pedestrian and its nearby neighbors. Second, we adopt an autoencoder feature-learning model to further enhance the representation of the extracted features. Finally, we utilize a Gaussian process regression model to infer the potential future trajectories of the target pedestrians given their walking history in the scene. We performed experiments using a challenging dataset, and our method yielded promising results and outperformed traditional methods proposed in the literature. [ABSTRACT FROM AUTHOR]

We present an agent-based approach to modeling crowd behavior that is based on complementary psychological and engineering principles. The application focus is for developing realistic models that address not only the physical but also the psychological aspects of crowd behavior. Our approach to modeling the psychology of a crowd is based on the principle of emotional reflection. According to this principle, human emotions are evoked in response to the perception of other people’s emotions, implying that emotions propagate in a crowd as a result of each person’s perception of other crowd members’ emotions in addition to external factors. We demonstrate that when incorporating an emotional component into a crowd simulation, there is enough sensitivity between the outcomes and emotion-based responses to provide a rich and powerful test-bed for assessing possible effects of emotionally driven responses in crowds. The emotional model is coupled with a movement model that is based on the social forces formulation, but with parameters that vary according to the current emotional state of each crowd member. We present the model along with results of how different emotional levels can affect the movement dynamics of crowds. [ABSTRACT FROM AUTHOR]

In this work, we propose an unsupervised approach for crowd scene anomaly detection and localization using a social network model. Using a window-based approach, a video scene is first partitioned at spatial and temporal levels, and a set of spatio-temporal cuboids is constructed. Objects exhibiting scene dynamics are detected and the crowd behavior in each cuboid is modeled using local social networks (LSN). From these local social networks, a global social network (GSN) is built for the current window to represent the global behavior of the scene. As the scene evolves with time, the global social network is updated accordingly using LSNs, to detect and localize abnormal behaviors. We demonstrate the effectiveness of the proposed Social Network Model (SNM) approach on a set of benchmark crowd analysis video sequences. The experimental results reveal that the proposed method outperforms the majority, if not all, of the state-of-the-art methods in terms of accuracy of anomaly detection. [ABSTRACT FROM AUTHOR]

One of the main objectives of crowd modeling is to optimize evacuation and improve the design of pedestrian facilities. In this work, a sensitivity analysis is performed to study the effect of the parameters of a 2D discrete crowd movement model on the nature of pedestrian's collision and on evacuation times. After presenting the proposed model in its full version (three degrees of freedom for each individual), a pedestrian-pedestrian collision is considered. We identified the parameters that govern this type of collision and studied their effects on it. Then an evacuation experiment of a facility with a bottleneck exit is introduced and its configuration is used for numerical simulations. It is shown that without introducing a social repulsive force, the obtained flow rate values are much higher than the experimental ones. For this reason, we introduced the social force as defined by Helbing and performed a parametric study to find the set of optimized values of this force's parameters that enables us to achieve simulation results close to the experimental ones. Using the values of the parameters obtained from the parametric study, the evacuation simulations give flow rate values that are closer to the experimental ones. The same optimized model is then used to find the density in front and inside the bottleneck and to reproduce the lane formation phenomenon as was observed in the experiment. Finally, the obtained results are analyzed and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach that utilizes multiple exemplar agent-based motion models (AMMs) to extract motion features (representing crowd behaviors) from the captured crowd trajectories. In the exemplar-based framework, we propose an iterative optimization algorithm to measure the correlation between any exemplar AMM and the trajectory data. It is based on the Extended Kalman Smoother and KL-divergence. In addition, based on the proposed correlation measure, we introduce the novel individual feature, in combination with the holistic feature, to describe crowd motions. Our results show that the proposed features perform well in classifying real-world crowd scenes. [ABSTRACT FROM AUTHOR]

In crisis situations in an urban environment, first responder teams often must deal with crowds of people. Consider the case of a building fire in a dense city environment. People may be injured; walkways may be blocked, with fire equipment attempting to reach the scene. Crowd behavior can become an issue when trying to reach the injured, ensure safety and restore conditions to normal. The motivations of pedestrians that form the crowd can vary. Some are there because they are curious about the crisis situation. Others, attending to their individual concerns, may have found themselves in the 'wrong' location. They may be trying to leave the area, but the density of people as well as the spatial layout of the walkways may be impeding their progress. Other individuals, unaware of the fire, may be attempting to reach their intended destinations that happen to be near the crisis area, thus adding to crowd congestion. With a model of crowd behavior, effective strategies for resource usage in managing crowd behavior can be developed. Our approach to this problem is that of agent-based modeling and simulation. We develop a cognitive pedestrian agent model. Utilizing this model, we simulate crowd behavior in a 'city fire' scenario. Characteristics of crowd behavior with different pedestrian personality mixes and a strategy for crowd management are investigated

Of the many crowd behavior models, very few have been used in assisting crowd management practice. This lack of usage is partly due to crowd management involving a diversity of situations that require competencies in observing, sense-making, anticipating and acting. Crowd research is similarly scattered across disciplines and needs integration to advance the field towards supporting practice. To address these needs, we present in C rowd , an integrated framework detailing a high-level architecture of a decision-support system for crowd management and model development. It also offers a lens for categorizing crowd literature, allowing us to present a structured literature review. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an integrated electronic system that allows the dynamical management of congestion and provides the fast evaluation of dynamical circumstances. Thus, a cellular-automata-based model is proposed that estimates the movement of individuals. The presented system incorporates a process that allows the efficient camera-based initialization of the model, without any special prerequirements. The efficiency of the model has been thoroughly validated. Specifically, simulation-derived diagrams that depict the relationship of flow and speed of people as a function of crowd density have been compared with corresponding diagrams from the literature. Furthermore, the system has been evaluated with the use of real data. In particular, simulation results have been compared with real video recordings that depict the crowd evacuation process from a football stadium. Results prove that the proposed management system can estimate fast possible routes of people for the very near future, evaluating all possible exit alternatives. Finally, the proposed model has been implemented in hardware with a field-programmable gate array, enabling its incorporation into an integrated electronic system that estimates crowd movement and prevents congestion in exits almost in real time. The proposed electronic system is advantageous in terms of easy incorporation and portability as well as performance when compared with its analogous graphical-processing-unit implementation. [ABSTRACT FROM PUBLISHER]

In this work, we provide an approach for Modeling and Simulation (M&S) of crowds using Cellular Discrete EVent System Specification (Cell-DEVS). We present many examples of Cellular Discrete EVent System Specification entity-based crowd models, and we show how to use Cellular Discrete EVent System Specification for entity-based modeling and simulation of crowds. We provide an approach for using Cellular Discrete EVent System Specification theory in modeling and simulation of crowds, and we propose Cellular Discrete EVent System Specification entity-based models for modeling and simulation of one-, two-, and three-dimensional movement of crowds. We extend the models above, and propose a more advanced model for crowd movement in multi-level building. Furthermore, we use this model for simulation of building evacuations. We propose another advanced model for crowd modeling, and deploy the model in occupancy analysis of buildings. Simulation results verify the usability of the proposed models. [ABSTRACT FROM AUTHOR]

Managing the uncertainties that arise in disasters - such as a ship or building fire - can be extremely challenging. Previous work has typically focused either on modeling crowd behavior, hazard dynamics, or targeting fully known environments. However, when a disaster strikes, uncertainties about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowds and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd and hazard dynamics, using ship- and building fire as proof-of-concept scenarios. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior, being based on studies of physical fire models, crowd psychology models, and corresponding flow models. Simulation results demonstrate that the DBN model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Our scheme thus opens up for novel in situ threat mapping and evacuation planning under uncertainty, with applications to emergency response. [ABSTRACT FROM AUTHOR]

Abstract: We investigate a general cognitive model of group behaviors, based on Festinger’s social comparison theory (SCT), a prominent social psychology theory. We describe two possible implementations of SCT process at an architectural level, on the basis of the Soar cognitive architecture. The first, which seems to follow directly from Festinger’s social comparison theory, treats the SCT process as an uncertainty-resolution method. The second, takes a different approach, in which an SCT process is constantly active, in parallel to any problem solving activity. We present the implementation of these approaches in the Soar cognitive architecture and argue that one is more suitable for modeling crowd behaviors. In previous work, we have shown that SCT covers a variety of pedestrian movement phenomena. In this paper we present the use of the SCT model in generation of imitational behavior in loosely-coupled groups. Based on experiments with human subjects, we show that SCT generates behavior in-tune with human crowd behavior. [Copyright &y& Elsevier]

This paper presents an anticipative system which operates during pedestrian evacuation processes and prevents escape points from congestion. The processing framework of the system includes four discrete stages: a) the detection and tracking of pedestrians, b) the estimation of possible route for the very near future, indicating possible congestion in exits, c) the proposal of free and nearby escape alternatives, and d) the activation of guiding signals, sound and optical. Detection and tracking of pedestrians is based on an enhanced implementation of a system proposed by Viola, Jones, and Snow that incorporates both appearance and motion information in near real-time. At any moment, detected pedestrians can instantly be defined as the initial condition of the second stage of the system, i.e., the route estimation model. Route estimation is enabled by a dynamic model inspired by electrostatic-induced potential fields. The model combines electrostatic-induced potential fields to incorporate flexibility in the movement of pedestrians. It is based on Cellular Automata (CA), thus taking advantage of their inherent ability to represent effectively phenomena of arbitrary complexity. Presumable congestion during crowd egress, leads to the prompt activation of sound and optical signals that guide pedestrians towards alternative escaping points. Anticipative crowd management has not been thoroughly employed and this system aims at constituting an effective proposal. [ABSTRACT FROM PUBLISHER]

We survey our recent work on interactive modeling, generation, and control of large-scale crowds and traffic for simulating digital cities. These include multi-agent navigation, simulating large crowds with emerging behaviors as well as interactive simulation of traffic on large road networks. We also highlight their performance on different scenarios. [ABSTRACT FROM AUTHOR]

Human behavior representation (HBR) models simulate human behaviors and responses. The Joint Crowd Federate™ cognitive model developed by the Virginia. Modeling, Analysis, and Simulation Center (VMASC) and licensed by WernerAnderson, Inc., models the cognitive behavior of crowds to provide credible crowd behavior in support of military training, exercise support, and wargaming experiments and can befederated with semi-automated forces (SAF) simulations. Other possible uses include training support to emergency management and security personnel. The large parameter spaces and behavior complexities associated with HBR can make validating these simulations difficult, but with a multipronged approach, it is not impossible. A comprehensive approach investigates not only the results of the simulation but also the mathematical instantiation in the conceptual model. This article demonstrates a technique developed to understand the mathematical instantiation of the parameter space embodied by our HBR model and the effects of different parameterizations on the model's initialization space. [ABSTRACT FROM AUTHOR]