Crowd Dynamics Research Articles

The Limo-Powered Crowd Monitoring System: Deep Life Modeling for Dynamic Crowd With Edge-Based Information Cognition

To ensure the safety of people in massively crowded activities, crowd monitoring systems are very important. Presently, many researches focus on studying video for crowd monitoring, and realize the recognition of behavior patterns. However, most systems only implement a real-time display of the monitored scene to comprehensively model a place. Because the environment at crowd gatherings is complex and dynamic, an intelligent monitoring system is necessary. This paper proposes a limo-powered crowd monitoring system for modeling dynamic crowds. On one hand, limo-system realizes life modeling at the individual level, offering personalized cognition of each person. On the other hand, it uses data collected by multi-sensor devices and intelligent algorithms deployed on the edge cloud to dynamically perceive the environment. At the same time, considering the redundancy of monitoring data, the value of information can be recognized via information cognition components in the edge cloud. Finally, combining the results of environment awareness and life modeling, simulation experiments of crowd scenes are executed for multi-scenarios. Results show that the intelligent system can be effectively applied to crowd monitoring in comprehensive environments, with important significance for crowd perception and modeling.

A kinetic theory approach for 2D crowd dynamics with emotional contagion

In this paper, we present a computational modeling approach for the dynamics of human crowds, where the spreading of an emotion (specifically fear) has an influence on the pedestrians’ behavior. Our approach is based on the methods of the kinetic theory of active particles. The model allows us to weight between two competing behaviors depending on fear level: the search for less congested areas and the tendency to follow the stream unconsciously (herding). The fear level of each pedestrian influences their walking speed and is influenced by the fear levels of their neighbors. Numerically, we solve our pedestrian model with emotional contagion using an operator splitting scheme. We simulate evacuation scenarios involving two groups of interacting pedestrians to assess how domain geometry and the details of fear propagation impact evacuation dynamics. Further, we reproduce the evacuation dynamics of an experimental study involving distressed ants.

A Multi-platform Study of Crowd Signals Associated with Successful Online Fundraising

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover universal crowd dynamics that ultimately decide which projects will succeed. In all analyses and across three markets, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications.

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

IMPROVED PARTICLE SWAM OPTIMIZATION FOR CROWD SIMULATION USING HYBRID AGENT REINFORCEMENT LEARNING ALGORITHM

In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation

Simulation of competitive and cooperative egress movements on the crowd emergency evacuation

ABSTRACT Empirical evidence suggests that evacuees tend to break down their well-coordinated motion and adopt competitive egress behaviour when they are confronting life-endangering situations (e.g., fire accident, earthquake). This phenomenon significantly influences crowd dynamics, and subsequently, the overall evacuation time. In this paper, a novel evacuation model has been developed, which takes the detailed cooperative and competitive egress movements of evacuees into consideration. The proposed model has been conducted to simulate crowd (i.e., with different proportion of competitive individuals within the crowd) evacuating from a single-exit compartment room (i.e., the exit width varies from 0.8 to 2.0m). Simulation results show that competitive evacuees exhibit an arc-shape formation in front of the bottleneck area (i.e., the exit). This is notably distinct from the cooperative evacuees generated shape formation. In addition, the evacuation performance (i.e., evacuation time, trajectories, density and flow rate) of competitive and cooperative evacuees over time are successfully captured for analysis. This study contributes to a better understanding of the crowd evacuation dynamics by considering the impact of competitive and cooperative egress movements.

Characteristic time in highly motivated movements of children and adults through bottlenecks

Current codes for fire protection of buildings are mainly based on the movement of adults and neglect the movement characteristic of pre-school children. Having a profound comprehension of the difference between children and adults passing bottlenecks is of great help to improve the safety levels of preschool children. This paper presents an experimental study on the bottleneck flow of pre-school children in a room. The movement characteristics of children’s and adults’ bottleneck flow are investigated with two macroscopic properties: density and speed profiles as well as microscopic characteristic time: motion activation time, relaxation time, exit travel time and time gap. Arch-like density distributions are observed both for highly motivated children and adults, while the distance between the peak density region and the exit location is shorter for children and longer for adults. Children’s movement is less flexible manifested as longer motion activation time and longer relaxation time compared to that of adults. The findings from this study could enhance the understanding of crowd dynamics among the children population and provide supports for the scientific building design for children’s facilities.

Emergent Strategy from Spontaneous Anger: Crowd Dynamics in the First 48 Hours of the Ferguson Shooting

The fatal August 9, 2014, officer-involved shooting of a Black teenager in Ferguson, Missouri, sparked a series of local protests that culminated in a national social movement: Black Lives Matter. In this study, through a minute-by-minute analysis of crowd dynamics, I find that the eventual social movement strategy emerged from spontaneous acts of anger in protest crowds within the first 48 hours of the shooting. This finding is surprising in light of social movement scholarship, in which strategy is thought to follow from rationality and decision making within formal organizations, not emotionality and spontaneous action within informal crowds. By coupling a historical analysis of protest and policing practices with a comparison of prominent theories of crowds, emotion, and strategy, I theorize how strategy can emerge from spontaneous acts of anger as part of a distributed sensemaking process in crowds, rather than conflating strategy with rationality and deliberate planning in organizations. Taken in sum, this study challenges prevailing ideas about the wisdom of crowds and exemplifies the immanent potential for change, in which our seemingly “micro” actions are not trivial but can influence even the most “macro” of strategic outcomes.

Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology.

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.

MACROSCOPIC FUNDAMENTAL FLOW DIAGRAMS OF A SPIRITUALLY MOTIVATED CROWD

Managing and monitoring large crowds pose a challenge to the crowd management authorities. Understanding the crowd dynamics in terms of its macroscopic parameters and its application to assess the maximum number of people likely to gather at a location or the number of people to be allowed to pass a point can go a long way in preventing situations of crowd risk. This paper presents a comparison of the fundamental diagrams of pedestrian flow at different locations of Kumbh mela and brings out differences in the flow patterns between a general crowd and a spiritually motivated crowd. The effect of spiritual motivation is reflected in terms of higher speeds of pedestrians at some locations and continuous flow despite higher densities. These results fill a significant research gap in the literature by highlighting the unique characteristics of spiritual crowd flows that must be incorporated for simulating a realistic crowd.

Characterization of different crowd behaviors using novel deep learning framework

Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model framework that automatically characterizes different crowd behaviors based on motion and appearance. We first extract dense trajectories from the input video segment and then generate trajectory image by projecting trajectories on to image plane. Trajectory image effectively captures relative motion in the scene. We use stack of trajectory images to train deep convolutional network that learns compact and powerful representation of motion in the scene. We evaluate our approach on UCF, CUHK, and Crowd-11 benchmark datasets. From the experiment results, we demonstrate, both in quantitative and qualitative ways, that the proposed framework outperforms other existing methods by a great margin.

Preparedness for Mass Gatherings: A Simulation-Based Framework for Flow Control and Management Using Crowd Monitoring Data

Proactive management at mass gatherings is vital to ensure safe crowd evacuation during emergencies. The increasing number of crowd incidents and casualties during the Hajj season is one of the major concerns for authorities in Saudi Arabia. This study aims to explore and analyze crowd dynamics visiting the Prophet's (PBUH) tomb at the visiting (Ziara) corridor in the Holy Mosque of Madinah under continuous flow conditions. MassMotion was used to optimize the crowd flow rate with density restricted to a safe threshold value for efficient crowd management. A robust regression model has been developed to guide the authorities for the safe and efficient operation of the visiting corridor. The study results showed that the crowd flow beyond 9200 persons/h and waiting time in excess of 42 s in front of Moajha might lead to breakdown condition. The output of this study can be utilized by decision-makers and concerned authorities to take appropriate and timely remedial actions to ensure safe, smooth, and efficient crowd management.

High fidelity modeling of aerosol pathogen propagation in built environments with moving pedestrians.

A high fidelity model for the propagation of pathogens via aerosols in the presence of moving pedestrians is proposed. The key idea is the tight coupling of computational fluid dynamics and computational crowd dynamics in order to capture the emission, transport and inhalation of pathogen loads in space and time. An example simulating pathogen propagation in a narrow corridor with moving pedestrians clearly shows the considerable effect that pedestrian motion has on airflow, and hence on pathogen propagation and potential infectivity.

The Notion of Validity in Experimental Crowd Dynamics

The Notion of Validity in Experimental Crowd Dynamics

A smart dynamic crowd evacuation system for exhibition centers

Abstract In this paper, we consider the problem of finding the safest evacuation route in a multi-exit exhibition center while the fire hazard spreads. We first propose a system composed of sensor nodes to collect pertinent safety data. We present a real-time dynamic evacuation system that considers the changing conditions in the risks associated with each hallway segment in terms of walking distance, heat, two major asphyxiant fire gases and congestion. Our system activates smart panels placed at major junctions of the hallways to guide evacuees towards the appropriate exit by displaying the proper escape direction. This work can pave the way towards the development of next-generation smart exhibition centers, where crowd safety is among the top priorities.

Modelling emergency evacuation from an industrial building under spreading fire using a social force model with fire dynamics

Abstract Study of human evacuation from industrial buildings under spreading fire is a challenging research area. Modelling evacuation dynamics under spreading fire is crucial to evaluate the crowd risk and safety in emergency evacuation. The paper presents a crowd dynamics model for emergency evacuation from an industrial building under spreading fire. This model is formulated by integrating a social force model with fire dynamics to investigate the evacuation process under spreading fire. The evacuation model was implemented in MATLAB to simulate the human dynamics for evacuation from a large single-story building under fire with a single exit door. The test results demonstrate that the overall evacuation time was affected by numerous factors for instance: fire spreading rate, crowd density and exit-width. The proposed model can be used to simulate the efficiency of the evacuation process from an industrial building, and thus, it can enhance the design of industries to increase the safety level.

Evolutionary Game of Emergency Evacuation After an Earthquake at a University: How to Promote Orderly Evacuation

Safe and effective evacuation is very important to reduce casualties after earthquakes. Given that evacuation is a checking process rather than a preselection process, evolutionary game theory can be used to better understand the crowd dynamics by analyzing the imitation effects in terms of individual interactions during evacuation. The key purpose of this paper was to construct an evolutionary game model of emergency evacuation of a teaching building at a university after an earthquake to search for the specific constraints necessary to reach the expected collective behaviors for orderly evacuation. We first analyzed the evacuation scenarios, including the environment of the teaching building, emergency degree of the earthquake and behaviors of the crowd. The related game players in the evacuation process were divided into three types, impulsive evacuees, calm evacuees and university staffs, and their payoff matrix was constructed according to the evacuation scenarios. Then, an evolutionary game model via the replicated dynamic system was created, and stable strategies were discussed. Next, we simulated eighteen evacuation scenarios with adjustment of the guiding authoritativeness, emergency degree, and group-oriented coefficient; the simulation results showed that the worst stable strategies can be avoided and the expected stable strategies can be obtained by adjusting the constraints, which are closely related to the corresponding management measures. Finally, the performance of the proposed model was verified by comparisons with the real videos and the conventional strategies.

Convergence of some Mean Field Games systems to aggregation and flocking models

For two classes of Mean Field Game systems we study the convergence of solutions as the interest rate in the cost functional becomes very large, modelling agents caring only about a very short time-horizon, and the cost of the control becomes very cheap. The limit in both cases is a single first order integro-partial differential equation for the evolution of the mass density. The first model is a 2nd order MFG system with vanishing viscosity, and the limit is an Aggregation Equation. The result has an interpretation for models of collective animal behaviour and of crowd dynamics. The second class of problems are 1st order MFGs of acceleration and the limit is the kinetic equation associated to the Cucker–Smale model. The first problem is analysed by PDE methods, whereas the second is studied by variational methods in the space of probability measures on trajectories.

A linear wave propagation‐based simulation model for dense and polarized crowds

AbstractFluid‐like motion and linear wave propagation behavior will emerge when we impose boundary constraints and polarized conditions on crowds. To this end, we present a Lagrangian hydrodynamics method to simulate the fluid‐like motion of crowd and a triggering approach to generate the linear stop‐and‐go wave behavior. Specifically, we impose a self‐propulsion force on the leading agents of the crowd to push the crowd to move forward and introduce a Smoothed Particle Hydrodynamics‐based model to simulate the dynamics of dense crowds. Besides, we present a motion signal propagation approach to trigger the rest of the crowd so that they respond to the immediate leaders linearly, which can lead to the linear stop‐and‐go wave effect of the fluid‐like motion for the crowd. Our experiments demonstrate that our model can simulate large‐scale dense crowds with linear wave propagation.

Incentive Mechanism for Mobile Devices in Dynamic Crowd Sensing System

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.