Crowd Behavior Research Articles

A Coupled SFM-ASCRIBE Model To Investigate the Influence of Emotions and Collective Behavior in Homogeneous and Heterogeneous Crowds

The understanding of crowd behavior dynamics holds immense significance in ensuring public safety across a range of situations, including emergency evacuations and large-scale events. Our research focuses on two primary objectives: investigating the impact of emotions on crowd movement and gaining valuable insights into collective behavior within crowds. To achieve this, we present a coupled model, incorporating an enhanced ASCRIBE model with an agent displacement model. We introduce heterogeneity into our model by incorporating specific mobility laws for different categories of panicked crowds, considering the influence of emotions on both speed and direction. Through numerical simulations, we analyze the model's parameters, observe the behavior of uniform crowds, and explore the collective dynamics within diverse crowds. By conducting comprehensive simulations and analyses, the findings from this study can contribute to the development of more effective crowd management strategies and emergency evacuation protocols.

Research on emergency evacuation behavior rules of pedestrians under fire and explosion accidents in chemical parks considering social relationships

Chemical parks present a high risk of sudden accidents such as fires and explosions, given the significant number of hazardous sources present. This is why emergency evacuation after accidents is so essential. The behavior of crowds during evacuation is complex, and social relationships among pedestrians can have differentiated effects on the evacuation process. However, most of the current crowd evacuation research has focused on life scenarios and natural disaster contexts. Less attention has been paid to the emergency evacuation of crowds affected by social relations in the context of chemical accident disasters. A comprehensive evacuation model that considers the dynamic evolution of crowd transfer both within and outside the factory area is proposed in this paper. Using the Anylogic software based on the social force model, this study constructs emergency evacuation scenarios for crowd in the context of fire and explosion accidents, and toxic gas leakage incidents. Specifically, this study analyzes the impact of three factors, namely inter-personal distance, group size, and proportion of different group sizes in the total population, on the evacuation efficiency. It has been found that when large groups of pedestrians are split into smaller ones for evacuation, the evacuation efficiency can be effectively improved. The study includes calculations of the lethal range of a toxic gas and simulated the process of relocating large crowds of people to nearby shelters. In addition, a strategy for deploying pedestrian groups based on social relationships was developed for the context of mass evacuation. These measures have the potential to optimize the evacuation efficiency of pedestrian groups in chemical parks, including expanding and modifying shelters, diverting dense crowds from residential plots, using interpersonal spacing of 0.1 m, and splitting large groups of pedestrians into smaller ones. The research results can provide a reference for decision making for large-scale group evacuation in chemical parks, and improve the emergency evacuation capability and local risk response capability in chemical parks.

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations

Intelligent traffic monitoring is a prominent topic of investigation due the emergence of advancements like the Internet interconnected Things and intelligent computers. Combining these technologies will make it easier to methods to aid in making better choices and accelerating urban growth. Intelligent sensing has come to the forefront in recent years due to its capacity to make calculated decisions on its own to address difficult issues. Automatic vehicles and smart gadgets are equipped with sensors that are part of an IoT-based system in order to recognize, gather, and transmit data. Artificial intelligence (AI)-based techniques allow machines to acquire knowledge and keep tabs on their surroundings through continuous sensing. Improvements in variable traffic control strategies for overcrowded cities have numerous positive outcomes, one of which is increased road safety. Since the sensors on which conventional dynamic controllers relied had their own shortcomings, we might use vision sensors (like cameras) to avoid these issues. Image and video-based computing has a lot of potential for measuring traffic volumes. A new traffic management system named Enhanced Transportation Technologies (ETT) is implemented to relieve congestion at the busy intersection after the old one was deemed to be inadequate. The term "intelligent transportation system" (ITS) refers to a group of transportation systems to keep drivers and passengers safe on the road and to facilitate autonomous mobility by optimizing control systems. To further improve urban planning, crowd behavior, and traffic forecasting, dependable AI models have been developed to work in tandem with ITS. Compared to controllers using conventional sensors, the proposed model has been shown through extensive simulations to reduce waiting time and increase movement speed on average.

Review Paper on IoT Thread Detection using Deep CNN Classifier

Abnormal activity will lead to uncommon changes in the crowd behavior. In other words, the crowd motion changes conform to certain rules for valid behaviors, while for abnormal events the motion changes are uncontrolled. The motion-changed rules to detect and localize abnormal behavior in crowd videos. Specifically, we first generate the motion patterns based on the descriptor of collectiveness. Then each frame pair is represented as a transfer matrix whose elements are the difference of a set of motion patterns. Thereafter, the motion-changed rules are constructed in the transformation space using a bag-of-words approach. Finally, the proposed approach measures the similarity between motion-changed rules and the incoming video data to examine whether the actions are anomalous. The approach is tested on the UMN dataset and a challenging dataset of crowd videos taken from the railway station. The experimental results demonstrate the effectiveness of the proposed method for detection abnormal behavior

Crowd Emotion Prediction for Human-Vehicle Interaction Through Modified Transfer Learning and Fuzzy Logic Ranking

In metropolitan environments, unmanned aerial vehicles (UAVs) equipped with video surveillance equipment can monitor crowd behavior and maintain public safety. In high-traffic areas where humans are more likely to make mistakes, a smart city needs modern technology to forecast the behavior of its residents. In order to improve citywide traffic flow, urban transportation systems (UTSs) monitor and learn how people behave in crowds. Using UAVs for video surveillance in smart cities, our research describes a unique way to assess crowd condition, which expands the scope of human-vehicle interactions. Moreover, we use fuzzy logic ranking to improve the system’s ability to detect anomalies in crowds. In order to improve decision-making, a novel deep transfer learning (DTL) technique is applied to the UAV’s received frames. A 98.5% accuracy rate, satisfactory performance, and robustness to population behavior are all characteristics of the proposed integrated model. In UTSs and urban areas, our novel intelligent system analyzes human behavior based on vehicle-human interactions. In areas with low and high traffic congestion, the modified ResNet (mResNet) architecture predicts the crowd’s condition based on fuzzy logic (FLA). Through decision-making based on accurate crowd conditions, best general paths can be selected using the algorithm.

A modified social force model for sudden attack evacuation based on Yerkes–Dodson law and the tendency toward low risk areas

Various evacuation models are the internal support for the movement of pedestrians. Emergencies that occur in public places have the characteristics of suddenness, massive and unpredictability. These characteristics impact pedestrians’ psychological states, ultimately shaping their behaviors. However, the existing models do not fully consider this aspect. Therefore, for sudden attack events, based on the social force model and Yerkes–Dodson law, this paper proposes a modified model which considers the positive and negative effects of psychological state on pedestrians’ behaviors in three dimensions: the realization degree of desired speed, the proximity of attacker to pedestrian, and crowdedness. It also considers the diminishment of repulsion and the attractiveness of low risk areas. A behavioral model based on the exit-reorientation mechanism is proposed to describe the behavior of low risk exit selection according to the relative location of the attacker. The improved model is validated quantitatively and qualitatively, respectively. Simulations based on this model can effectively reproduce the behaviors of crowds in videos. The data of fundamental diagrams from our model fits well with that from videos. We find that the evacuation time is longer when the observation distance within a specific range in the simple scenario. The risk tolerance level of 8 m and 6 m are the critical points affecting the rate of change of survival rate and casualties, respectively. Simulation results indicate that the effect of rational people ratio on evacuation time will vary with risk tolerance level. This study offers insights into the dynamics of crowd evolution during an evacuation following a sudden attack, providing a foundation for exit allocation and emergency crowd guidance.

Real-Time Spatial-Temporal Depth Separable CNN for Multi-Functional Crowd Analysis in Videos

Crowd behavior prediction (CBP) and crowd counting (CC) are the essential functions of vision-based crowd analysis (CA), which play a crucial role in controlling crowd disasters. The CA using different models for the CBP and the CC will increase computational overheads and have synchronization issues. The state-of-the-art approaches utilized deep convolutional architectures to exploit spatial-temporal features to accomplish the objective, but such models suffer from computational complexities during convolution operations. Thus, to sort out the issues as mentioned earlier, this paper develops a single deep model which performs two functionalities of CA: CBP and CC. The proposed model uses multilayers of depth-wise separable CNN (DSCNN) to extract fine-grained spatial-temporal features from the scene. The DSCNN can minimize the number of matrix multiplications during convolution operation compared to traditional CNN. Further, the existing datasets are available to accomplish the single functionality of CA. In contrast, the proposed model needs a dual-tasking CA dataset which should provide the ground-truth labels for CBP and CC. Thus, a dual functionality CA dataset is prepared using a benchmark crowd behavior dataset, i.e. MED. Around 41[Formula: see text]000 frames have been manually annotated to obtain ground-truth crowd count values. This paper also demonstrates an experiment on the proposed multi-functional dataset and outperforms the state-of-the-art methods regarding several performance metrics. In addition, the proposed model processes each test frame at 3.40 milliseconds, and thus is easily applicable in real-time.

Катастрофическое сознание: краткий обзор проблемы

This article presents the relevant for the modern worldview sphere of philosophical reflection on the interaction between consciousness and reality as well as on the way thinking matches its content. The author takes a historical look at the ideas concerning the controversial problems of consciousness, from the views of Confucius and Socrates to the reasoning of modern authors about certain aspects of consciousness leading all subsequent human activity to catastrophic results. In the context of this issue, various approaches to the interaction of human and society with the outside world are demonstrated, as well as the historically developed views on harmonization (the idea of the cosmos as an orderly system of world order in ancient Greece and the laws of Heaven in Chinese pre-philosophy). The paper traces the origins of understanding those aspects of people’s attitude towards property in which one can find the basis for the catastrophic consciousness characteristic of our time. The formation and functioning of “names” in the system of existence is considered, which is presented in the theoretical principles developed by A.F. Losev. In addition, the paper analyses ideas about the possibility of achieving harmony in human relationships in the system of social existence and in the psychology of behaviour of crowds and individuals, as well as the existential views of the latter leading to actions with catastrophic attitudes. Moreover, the theoretical positions of D.S. Sommer and M.K. Mamardashvili’s school are compared. V.P. Vizgin’s article on catastrophic thinking and its main forms of manifestation is considered in detail. This paper uses comparative methods, historical analysis and interdisciplinary synthesis in combination with the techniques of abstraction and analogy

Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App Updates

Modern technology innovations feature a successive and even recurrent procedure. Intervals between old and new generations of technology are shrinking, and the Internet and Web services have facilitated the fast adoption of an innovation even before the convergence of its predecessor. While the adoption and diffusion of innovations have been studied for decades, most theories and analyses focus on single and one-time innovations. Meanwhile, limited work has investigated successive innovations while lacking user-level analysis, possibly due to the unavailability of fine-grained adoption behavior data. In this study, we present the first large-scale analysis of the adoption of recurrent innovations in the context of mobile app updates, investigating how millions of users consume various versions of thousands of apps on their mobile devices. Our analysis reveals novel patterns of crowd and individual adoption behaviors, which suggest the need for new categories of adopters to be added on top of the Rogers model of innovation diffusion. We show that standard machine learning models are able to pick up various sources of signals to predict whether users in these different categories will adopt a new version of an app and how soon they will adopt it.

One, Two, Three! Can Everybody Hear Me? Acoustics of Roman Contiones. Case Studies of the Capitoline Hill and the Temple of Bellona in Rome

Abstract Rhetoric was one of the cornerstones of Roman education and public speaking, the essence of being a Roman politician. The speakers attempted to captivate the audience with their style and convince them of their arguments. Studying the audience is therefore just as important as investigating the speakers and their speeches. The aim of this article is to estimate the number of people who could intelligibly hear a speaker delivering a speech from two speaking platforms located in the city of Rome: the podium of the Temple of Bellona in the Campus Martius (in the Late Republican and Late Augustan periods) and the Capitoline Temple. To do this, we built virtual reconstructions of both venues according to the current state of knowledge about them, taking into account the geometry of the space as well as the materials from which they were built. On the models thus prepared, we carried out acoustic simulations for three different levels of background noise (36, 49, and 55 dBA), resulting in Speech Transmission Index maps. The results became the basis upon which we estimated the size of the maximum potential crowds that could hear speech intelligibly, using two methods based on the behaviour of contemporary crowds. We further compared our results with those of previous studies that concern other speaking platforms in Rome.

A Survey on Age and Gender Detection Using Deep Learning

Abstract: The age and gender information are very important for various real world applications such as social understanding, biometrics, identity verification, crowd behavior analysis etc. There are numerous works proposed for age and gender prediction in the past several years. The age and gender detector project aims to develop an advanced AI system capable of accurately determining the age and genders of individuals from images. As we can see, the model outputs are mostly special parts as well as wrinkles, which are important for age and gender prediction. This project utilizes deep learning techniques to analyze special features and patterns, enabling real time identification and classification of age and gender attributes.

Dynamic Disturbance Propagation Model of Pedestrian Panic Behaviors and Lyapunov-Based Crowd Stability Analysis

In public places, pedestrian panic behaviors have received increasing attention due to their dangerous impact on normal pedestrian flow. To address this issue, this study considered crowd panic behaviors as two-dimensional Gaussian disturbances quantitatively triggered by accidents and analyzed the stability of the pedestrian crowd based on Lyapunov criterion. First, this study established a two-dimensional static model for the disturbance pressure in a crowd. Then, a dynamic disturbance–propagation model (DPM) of crowd panic behaviors was proposed based on the conservation law of fluid dynamics. The anisotropy of the disturbance pressure propagation was proven with theoretical derivations and simulation experiments, which kept consistent with ground truth. Further, a stability criterion was proposed for pedestrian crowd flow under disturbances based on Lyapunov theory. To validate the proposed DPM, we simulated a disturbance scenario in the waiting hall of Shanghai Hongqiao Railway Station. Subsequently, the visual disturbance propagation dynamics and crowd state evolution due to a panic behavior disturbance in a pedestrian crowd were investigated; Finally, the experimental results demonstrated that disturbance pressures and pedestrian density fluctuated and diffused with the panic behavior outbreak point as the disturbance center, showing heterogeneous characteristics. This study shows how we can locate the high-risk areas affected by pedestrian panic behaviors in advance, and further help control crowd flow to keep a pedestrian crowd safe in public buildings.

Exploring the Impacts of COVID-19 and Lockdown on Online Car-Hailing Travel in Shanghai

The COVID-19 pandemic and lockdown have caused serious impacts on people’s lives, especially on daily travel like online car-hailing. Understanding the impacts of the pandemic on online car-hailing travel is essential for sustainable urban planning and governance, especially during public health emergencies including COVID-19. However, few studies have delved into the in-depth patterns and interpretations of crowd behaviors and mobility variations resulting from the lockdown, especially from different perspectives. This study attempts to make contributions to this gap by building a three-step method from a macroscopic to mesoscopic perspective. A dataset of online car-hailing trajectories for 15 days in 2018 and 3 special days (before and after the lockdown) in 2022 was used. Detailed analyses of the overall spatiotemporal patterns, the flows between administrative districts, and the four-perspective investigation in the central urban area were conducted. The main findings include a dramatic plunge in ride counts for online car-hailing due to the lockdown and a significant change in human mobility associated with hospitals and traffic hubs. Our study provides insights into the understanding of impacts of COVID-19 and lockdown and hopefully helps with the resilience and sustainability of the city. The workflow might also be inspiring for further studies.

Analysis of Community Outdoor Public Spaces Based on Computer Vision Behavior Detection Algorithm

Community outdoor public spaces are indispensable to urban residents’ daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design.

Recurrence Quantification Analysis of Crowd Sound Dynamics.

When multiple individuals interact in a conversation or as part of a large crowd, emergent structures and dynamics arise that are behavioral properties of the interacting group rather than of any individual member of that group. Recent work using traditional signal processing techniques and machine learning has demonstrated that global acoustic data recorded from a crowd at a basketball game can be used to classify emergent crowd behavior in terms of the crowd's purported emotional state. We propose that the description of crowd behavior from such global acoustic data could benefit from nonlinear analysis methods derived from dynamical systems theory. Such methods have been used in recent research applying nonlinear methods to audio data extracted from music and group musical interactions. In this work, we used nonlinear analyses to extract features that are relevant to the behavioral interactions that underlie acoustic signals produced by a crowd attending a sporting event. We propose that recurrence dynamics measured from these audio signals via recurrence quantification analysis (RQA) reflect information about the behavioral dynamics of the crowd itself. We analyze these dynamics from acoustic signals recorded from crowds attending basketball games, and that were manually labeled according to the crowds' emotional state across six categories: angry noise, applause, cheer, distraction noise, positive chant, and negative chant. We show that RQA measures are useful to differentiate the emergent acoustic behavioral dynamics between these categories, and can provide insight into the recurrence patterns that underlie crowd interactions.

Relative rate of expansion controls speed in one-dimensional pedestrian following.

Patterns of crowd behavior are believed to result from local interactions between pedestrians. Many studies have investigated the local rules of interaction, such as steering, avoiding, and alignment, but how pedestrians control their walking speed when following another remains unsettled. Most pedestrian models assume the physical speed and distance of others as input. The present study compares such "omniscient" models with "visual" models based on optical variables. We experimentally tested eight speed control models from the pedestrian- and car-following literature. Walking participants were asked to follow a leader (a moving pole) in a virtual environment, while the leader's speed was perturbed during the trial. In Experiment 1, the leader's initial distance was varied. Each model was fit to the data and compared. The results showed that visual models based on optical expansion (\(\dot{\theta }\)) had the smallest root mean square error in speed across conditions, whereas other models exhibited increased error at longer distances. In Experiment 2, the leader's size (pole diameter) was varied. A model based on the relative rate of expansion (\(\dot{\theta }/\theta \)) performed better than the expansion rate model (\(\dot{\theta }\)), because it is less sensitive to leader size. Together, the results imply that pedestrians directly control their walking speed in one-dimensional following using relative rate of expansion, rather than the distal speed and distance of the leader.

Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle—A Case Study of Chengdu

Determining the optimal planning scale for urban life circles and analyzing the associated built environment factors are crucial for comprehending and regulating residential differentiation. This study aims to bridge the current research void concerning the nonlinear hierarchical relationships between the built environment and residential differentiation under the multiscale effect. Specifically, six indicators were derived from urban crowdsourcing data: diversity of built environment function (DBEF1), density of built environment function (DBEF2), blue–green environment (BGE), traffic accessibility (TA), population vitality (PV), and shopping vitality (SV). Then, a gradient boosting decision tree (GBDT) was applied to derive the analysis of these indicators. Finally, the interpretability of machine learning was leveraged to quantify the relative importance and nonlinear relationships between built environment indicators and housing prices. The results indicate a hierarchical structure and inflection point effect of the built environment on residential premiums. Notably, the impact trend of the built environment on housing prices within a 15 min life circle remains stable. The effect of crowd behavior, as depicted by PV and SV, on housing prices emerges as the most significant factor. Furthermore, this study also categorizes housing into common and high-end residences, thereby unveiling that distinct residential neighborhoods exhibit varying degrees of dependence on the built environment. The built environment exerts a scale effect on the formation of residential differentiation, with housing prices exhibiting increased sensitivity to the built environment at a smaller life circle scale. Conversely, the effect of the built environment on housing prices is amplified at a larger life circle scale. Under the dual influence of the scale and hierarchical effect, this framework can dynamically adapt to the uncertainty of changes in life circle planning policies and residential markets. This provides strong theoretical support for exploring the optimal life circle scale, alleviating residential differentiation, and promoting group fairness.

Enhancing Urban Spaces through Crowd Behavior Analysis A Case Study of Arafat to Muzdalifah Road During Hajj Time

Enhancing Urban Spaces through Crowd Behavior Analysis A Case Study of Arafat to Muzdalifah Road During Hajj Time

Thermal–Acoustic Interaction Impacts on Crowd Behaviors in an Urban Park

As urbanization accelerates, parks, as vital urban public open spaces, and their acoustic and thermal ambience directly impact visitors’ comfort and the sustainability of parks. Selecting Xihu Park in Fuzhou, China located in the subtropical region as a typical example, this study utilizes covert observational experiments with different typical sounds (grass cutting, music, and no sound source) across temperature levels to examine the influence of thermal–acoustic interactions on crowd behaviors in the park. The findings are as follows: (1) melodious music can attract more tourists, while strong stimulating grass cutting noises under high temperatures reduce crowd flow. Excluding unpleasant audio sources, park soundscapes across temperatures have a relatively limited influence on attractiveness to people flow. (2) High temperatures diminish tourists’ interest in landscape experiences and persons staying, especially when the soundscape quality is poorer. Under non-high temperatures, audio environments have a minor impact on the staying time. (3) The soundscape quality plays a role by affecting people’s path choices of approaching or avoiding sound sources, where grass cutting noise has the most negative influence. Music, grass cutting sounds, and natural sounds demonstrate conspicuous differences in their effects under varied temperatures. (4) Comfortable acoustic environments can draw larger crowds and decrease the walking pace. High temperatures make crowds take slower steps. Different sound types have significant influences on crowd movement velocity under three typical temperature levels. This study comprehensively investigates the mechanisms of typical thermal–acoustic environments’ impacts on park crowd behaviors, providing important references for optimizing the acoustic and thermal environments of urban parks, while also enriching related research on landscape design and environmental psychology. Future studies can conduct in-depth explorations by creating more abundant thermal–acoustic combinations and probe differences across diverse populations.

Behavioral Finance Provides Insights into Risk Perception and Risk Management

This article aims to explore the significant implications of behavioral finance in the realms of risk perception and risk management. Behavioral finance reveals behavioral biases exhibited by individuals in the investment and decision-making processes, uncovering phenomena that traditional financial theories have not fully explained. From overconfidence to loss aversion, and from herd behavior to delayed gratification, these behavioral biases influence investors' risk perception, risk aversion, and risk management strategies, thus triggering significant volatility and opportunities in the market. This article will first review the fundamental concepts and theoretical framework of behavioral finance, explaining why it holds particular significance in understanding risk perception and risk management. Subsequently, it will delve into how risk perception plays a pivotal role in investment decisions and analyze the impact of information processing on risk assessment. Building upon this foundation, it will delve into the specific applications of behavioral finance in risk perception and risk management, including loss aversion and loss-avoidance strategies, as well as the influence of crowd behavior on market risk.