Crowd Management Strategies Research Articles

New insights into the merging behavior of pedestrians: Quantification of lane competition level

Merging behavior is a complex crowd movement pattern caused by certain architectural features and has triggered serious crowd accidents worldwide. It is not uncommon to find such merging structures on the floor plans of some underground infrastructures, which deserve more detailed studies on special crowd movement patterns to enhance the safety level. Researchers have demonstrated that the unstable emotions and competitive behaviors of pedestrians during underground evacuation need to be taken into account due to the enclosed environment. However, there are no relevant studies focused on the competition characteristics among pedestrians during the merging process in a quantitative way. The comprehensiveness and systematization of the geometric features of typical merging structures, as well as the way to create the perception of competition and conflict among pedestrians during real emergencies also need to be improved. In this paper, we carried out a series of controlled experiments in symmetric and asymmetric merging structures with different channel widths and incentive levels to investigate crowd merging behavior with competition characteristics. The density and delay time within the merging area, as well as the time interval and lane changes before and after merging are analyzed. By introducing and modifying Simpson’s Diversity Index, we proposed a new method for quantifying the competition level among pedestrians during the merging process. It is concluded that the performance of the merging area is sensitive to the channel widths and the merging layouts, and the negative effect of unbalanced walking priority is more significant under high incentive level. The lane competition level during the merging process is correlated with the effects of various geometric features, movement layouts, and incentive levels. The method proposed in this paper brings new insights into the interpretation of merging scenarios and more studies on pedestrian dynamics. We hope that this study could serve as supplementary work for other modeling or field experiment methods to jointly provide more insights into the crowd management strategies in some underground infrastructures.

Detecting self-organising patterns in crowd motion: effect of optimisation algorithms

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios.

A novel Voronoi-based convolutional neural network framework for pushing person detection in crowd videos

Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: (i) feature extraction and (ii) video detection. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings demonstrate that the proposed framework outperforms state-of-the-art approaches, as well as seven baseline methods used for comparative analysis.

Nonreciprocal interactions in crowd dynamics: Investigating the impact of moving threats on pedestrian speed preferences

Nonreciprocal interaction crowd systems, such as human–human, human–vehicle, and human–robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains limited. Thus, in our study we explore this underresearched area, focusing on scenarios where nonreciprocity plays a critical role, such as mass stabbings, which pose a substantial risk to public safety. We conducted the first experiments on this system and analysed high-accuracy data obtained from these experiments. Specifically, we conduct laboratory experiments on three scenarios: single exits, dual exits, and obscured chases. Then, we introduce an equation to describe the mechanism behind a direct threat zone, which significantly affects pedestrian behaviour. The extent of the direct threat zone is determined by the speed of the moving threat and the radius of danger occurrence. The equation can provide insights for different nonreciprocal interaction crowd systems, where the subjects of the nonreciprocal interactions can be a pedestrian, an autonomous robot or a vehicle. We further categorize potential threats into direct, adjacent, and rear-view zones, quantifying the level of threat for pedestrians. Our study revealed that a pedestrian’s desired velocity correlated positively with potential threat intensity, increasing until near the direct threat zone. An emerging steady state is observed when escape routes are blocked by moving threats. This deviation affects the density–velocity relationship, making it distinct from the general relationship. This deviation signifies unique pedestrian behaviour in the presence of moving threats. Additionally, the rate of change in the angle for pedestrian motion in various desired directions is synchronized. This indicates the emergence of collective intelligence in nonreciprocal interaction crowd systems. As a result, our study may constitute a pioneering step towards understanding nonreciprocal interactions in crowd systems through laboratory experiments. These findings may enhance pedestrian safety and inform not only government crowd management strategies but also individual self-protection measures.

Modelling crowd pressure and turbulence through a mixed-type continuum approach

Empirical studies of large gatherings and natural disasters have revealed two important features of dense crowds: extremely high crowd pressure and crowd turbulence. In this study, a mixed-type continuum model for multidirectional pedestrian flow was developed that explicitly considered the phase transition of different anticipation characteristics under different densities. Non-hyperbolicity was used to model the strong instabilities during crowd turbulence. In addition, by estimating the aggregated crowd pressure, the proposed model could clarify the effects of both force chains and panic sentiment, phenomena commonly observed during crowd disasters. The non-hyperbolic partial differential equations were solved using the mixed-type finite difference method, and Eikonal equations were solved using the fast sweeping method. Subsequently, the continuum model was applied to simulations of two real-world scenarios – the 2015 Hajj crowd disaster and the 2010 Love Parade crowd disaster – and validated through comparison with empirical observations. Overall, the proposed model is an efficient tool for evaluating crowd management strategies to predict and assess the crowd state.

Collision avoidance behaviours of luggage-laden pedestrians

Understanding how pedestrians move and avoid collisions is essential for ensuring safety in crowded environments. In this study, we conducted detailed experiments, focusing on variables such as movement type (walking and running), encounter angles (90 and 180 degrees), and pedestrian characteristics. We analyzed a total of 168 trajectories to gain insights into the collision avoidance strategies employed by luggage-laden pedestrians. Our study highlights the significant impact of luggage on the minimum avoidance distance in vertical encounters, while head-on approaches show limited influence. We use the minimum predicted distance to evaluate time avoidance time, with particular focus on the presence of luggage as a pivotal determinant. Furthermore, we conduct a comprehensive categorization on pedestrian behaviors when carrying luggage, taking into account their speed and trajectory. Our findings reveal distinct strategies adopted by luggage-laden pedestrians when avoiding from the left and right sides. Interestingly, pedestrians employ more efficient conflict mitigation tactics when interacting with luggage-laden individuals approaching from the left compared to the right side. These insights are valuable for optimizing crowd management strategies, particularly in transportation hubs with luggage-laden crowds.

Crowd Management, Risk Assessment Strategies and Sports Tourism Events in Nairobi County, Kenya

The lack of long-term, sustainable strategic planning and development for sports and tourism in Kenya is a challenge for Kenya's sports tourism industry. The purpose of the study was thus to determine the impact of risk assessment methodologies on sporting tourism events. The specific objectives of the study were: to create a profile of the sports tourism events and venues, to establish the effect of crowd management and risk assessment strategies on sports tourism events in Nairobi County, Kenya; and to determine the moderating effect of financial capability on the relationship between crowd management, risk assessment strategies, and sports tourism events in Nairobi County, Kenya. In this study, a descriptive research approach was utilized, and the population of interest consisted of 17 prominent sporting event venues located within Nairobi County. The research focused its attention on a total of 340 participants in addition to 69 managers. The participants in the study, which consisted of 17 managers and 105 sportspeople, were chosen using a technique called purposeful sampling. In order to obtain data that reflect the ideas and opinions of the respondents in an unfiltered manner, questionnaires and an interview guide were used. A linear regression model was used to link the independent variable to the dependent variable. Result outputs were represented in tables, charts, and bars. The multiple linear regression analysis findings revealed a coefficient of determination of 0.700, implying that the independent variable used in this study; crowd management and risk management strategies, explains 70% of the variation in sports tourism events in Nairobi County, Kenya. The study established that there was a positive and statistically significant relationship between Crowd management and risk management strategies and the success of sports tourism events in Nairobi County (β=0.837, p=0.000<0.05). The study also established that financial capability had moderating effect on the relationship between crowd management and risk assessment strategies and the success of sports tourism events in Nairobi County. The study thus recommends a multi-agency approach to strengthen risk assessment and crowd management protocols. Keywords: Crowd Management, Sports Tourism Events, Risk Assessment Strategies, Kenya Sports Tourism Safety, Nairobi County Sports Events

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.

The Analysis and AI Simulation of Passenger Flows in an Airport Terminal: A Decision-Making Tool

In this paper, a decision-making tool is proposed that can utilize different strategies to deal with passenger flows in airport terminals. A simulation model has been developed to investigate these strategies, which can be updated and modified based on the current requirements of an airport terminal. The proposed tool could help airport managers and relevant decision makers proactively mitigate potential risks and evaluate crowd management strategies. The aim is to eliminate risk factors due to overcrowding and minimize passenger waiting times within the terminal to provide a seamless, safe and satisfying travel experience. Overcrowding in certain areas of the terminal makes it difficult for passengers to move freely and increases the risk of accidents (especially in the event of an emergency), security problems and service interruptions. In addition, long queues can lead to frustration among passengers and increase potential conflicts or stress-related incidents. Based on the derived results, the optimized routing of passengers using modern technological solutions is the most promising crowd management strategy for a sample airport that can handle 800 passengers per hour.

MANAJEMEN HAJI DAN UMRAH DALAM PEMETAAN BIBLIOMETRIK

This research aims to analyze research trends in the field of Hajj and Umrah management from researchers around the world. Through bibliometric analysis, this research provides an overview of research maps formed from keywords and important issues analyzed using the VOSviewer application. Data was obtained from the Dimensions.ai database, with the search keyword "Hajj and Umrah Management". The research results found that research on Hajj and Umrah management has increased every year. Writers from Indonesia and Arabic are the major contributors in this studies. The results of the cluster analysis found six theme clusters, cluster 1 discussed the theme of crowd management strategies. Cluster 2, regarding special service aspects for groups of Hajj and Umrah pilgrims. Cluster 3 shows a close relationship between the themes of the Ministry of Religion, Policy and Transparency. Cluster 4 underlines the issue of customer satisfaction, the role of related institutions and Umrah services in Indonesia. Meanwhile, cluster 5 groups issues regarding the application of technology, applications and information systems in Hajj management. And cluster 6, reflects the relationship between the COVID-19 Pandemic and Hajj and Umrah Management.

Understanding pedestrian route choice behavior in the continuous space: Diversity and equilibrium

Exploring general rules from diverse motion choices in the real world is a pivotal area of study that deepens our understanding of pedestrian behavior. In this research, we conduct pedestrian circle antipode experiments, from which we extract pedestrians' trajectories from repeated trials. We focus on the symmetric features identified from these experiments and employ the K-Medoids trajectory clustering method alongside several behavioral indicators, namely route length, travel time, and comfort level, to distinguish and compare different trajectory route choice patterns. In doing so, we delve into the heterogeneous behaviors exhibited by pedestrians within a single experiment and the stochastic behaviors seen across different experiments for a single pedestrian. Additionally, we establish a linear model to scrutinize the equilibrium relationship among these behavioral indicators, and further deploy the Gaussian process to shed light on the equilibrium characteristics inherent in pedestrian route choice behavior. Our findings suggest that despite the inherent heterogeneity and stochastic nature of individual behaviors, pedestrian route choice overall operates in a dynamic equilibrium state. These insights offer valuable implications for the design of facilities in transport hubs and crowd management strategies in social activities, potentially leading to more effective and organized public spaces.

Ranking and Analysis the Strategies of Crowd Management to Reduce the Risks of Crushes and Stampedes in Crowded Environments and Ensure the Safety of Passengers


 Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to deal with uncertain and vague information in the evaluation process. There are various factors in ranking the various strategies. So, the concept of multi-criteria decision-making (MCDM) is used to deal with various criteria. The neutrosophic set is integrated with the MCDM methodologies to rank various strategies. This study used the analytical hierarchy process (AHP) method to compute the weights of factors. Then the technique for order preference by similarity to the ideal solution (TOPSIS) method is used to rank the various strategies. An application was conducted to apply the proposed method. The outcome shows the safety and security factor is the heights important. The sensitivity analysis is applied to show the rank of strategies under various weights of factors. Finally, comparative analysis is applied to show the robustness of the proposed method compared with other MCDM methods.

How COVID-19 is Affecting Pedestrian Modeling and Simulation: The Case of Venice

The use of computer-based pedestrian crowd simulations has become crucial in transport planning considering the unprecedented effects of the COVID-19 pandemic on urban mobility. However, there is still a lack of knowledge in relation to the impact of social distancing on crowding, queuing, route choice, and other pedestrian crowd phenomena. In this context, the aim of the current study was to apply the Social Force Model of the pedestrian simulation platform PTV Viswalk to investigate the effects of disruption of social distancing on pedestrian dynamics. First, a descriptive set of metrics and parameters was applied for calibrating the dynamic regulation of interpersonal distances among pedestrians. The plausibility of the proposed social distancing model was then evaluated against the so-called fundamental diagram to calibrate pedestrian volume-delay functions. Finally, the proposed model was integrated into the PTV Visum simulation platform to evaluate the effects of social distancing on large-scale pedestrian route choice. To do so, a macroscopic static model of the city of Venice was developed to test the effectiveness of alternative crowd management strategies related to pedestrian dynamics in a predictive scheme.

Testing and evaluation of crowd management strategies at religious gatherings in India using agent-based modelling and simulation

Testing and evaluation of crowd management strategies at religious gatherings in India using agent-based modelling and simulation

Applying Crowd Risk Mitigation Technologies in Urban Sport Events

In recent years, the need for advanced precautions for mitigating the risks imposed by events, which involve high volumes of people in shared spaces, has multiplied. The occurrence of COVID-19 pandemic has further altered event practices, spaces, and event attendees’ mindsets in large-scale events. Proper crowd management not only seeks to prevent acts of violence and injury, but in today’s event environments; efforts should be consciously applied to reduce the spread of respiratory infections such as COVID-19. As the events industry continues to evolve and face new limitations, ways in which event organizers respond must evolve as well. Smartphone technologies are opening new ways for event organizers to communicate with and monitor attendees. This case study explores current crowd management strategies, analyzes the gaps in widely used models, and finally proposes event management technologies trending in the field.

Self-reported likely behaviour of rail passengers during an emergency evacuation-A case study of Kuala Lumpur, Malaysia

Prior knowledge on how passengers behave during a potential emergency evacuation could be advantageous in designing efficient crowd management and emergency procedures. This study examines the likely behaviours of Malaysian passengers during a potential future emergency evacuation situation. Four key behaviours, i.e., reactive, proactive, cooperative and competitive behaviours, were considered. A questionnaire survey was conducted at one major rail transit terminal in Kuala Lumpur, Malaysia to collect the data. All 301 questionnaire responses displayed non-uniformity in their likely behaviour. Results explained that the passengers are more likely to be proactive (e.g., move to exit immediately as soon as the warning siren is sounded) than reactive (e.g., go to assembly point after being instructed) during the pre-evacuation. Further, people are more likely to be cooperative (e.g., help other people who may have difficulties in getting out) than competitive (e.g., push or shove other peoples to get out quickly) during the evacuation. In terms of demographic influences on behaviours, results demonstrated that there could be significant differences in certain behaviours between males and females and between different age groups. The findings of this study provide valuable information for developing models for simulating passengers' evacuation at rail transit terminals. Further, the managers of emergency response could utilize such data and outcomes in devising effective crowd management strategies and developing appropriate training and educational campaigns.

Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis

Protest demonstrations are a manifestation of fundamental rights. Authorities are responsible for guiding protesters safely along predefined routes, typically set in an urban built environment. Microscopic crowd simulations support decision-makers in finding sustainable crowd management strategies. Planning routes usually requires knowledge about the length of the demonstration march. This case study quantifies the impact of two uncertain parameters, the number of protesters and the standard deviation of their free-flow speeds, on the length of a protest march through Kaiserslautern, Germany. Over 1000 participants walking through more than 100,000 m2 lead to a computationally demanding model that cannot be analyzed with a standard Monte Carlo ansatz. We select and apply analysis methods that are efficient for large topographies. This combination constitutes the main novelty of this paper: We compute Sobol’ indices with two different methods, based on polynomial chaos expansions, for a down-scaled version of the original set-up and compare them to Monte Carlo computations. We employ the more accurate of the approaches for the full-scale scenario. The global sensitivity analysis reveals a shift in the governing parameter from the number of protesters to the standard deviation of their free-flow speeds over time, stressing the benefits of a time-dependent analysis. We discuss typical actions, for example floats that reduce the variation of the free-flow speed, and their effectiveness in view of the findings.

Testing and Evaluation of Crowd Management Strategies at Religious Gatherings in India using Agent Based Modelling and Simulation

Testing and Evaluation of Crowd Management Strategies at Religious Gatherings in India using Agent Based Modelling and Simulation

Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality.

Dense crowds in public spaces have often caused serious security issues at large events. In this paper, we study the 2010 Love Parade disaster, for which a large amount of data (e.g. research papers, professional reports and video footage) exist. We reproduce the Love Parade disaster in a three-dimensional computer simulation calibrated with data from the actual event and using the social force model for pedestrian behaviour. Moreover, we simulate several crowd management strategies and investigate their ability to prevent the disaster. We evaluate these strategies in virtual reality (VR) by measuring the response and arousal of participants while experiencing the simulated event from a festival attendee’s perspective. Overall, we find that opening an additional exit and removing the police cordons could have significantly reduced the number of casualties. We also find that this strategy affects the physiological responses of the participants in VR.

Experimental study and analysis on behaviours and strategies of social groups and individuals

Multi-directional flows in pedestrian facilities involve several interactions and conflicts among pedestrians. To understand how one reaches his/her destination in a crowd with multidirectional movement, a series of experiments were performed at different densities by considering social groups. No significant difference was observed in speed between individuals and social groups in the experiment. However, the length of the walk paths for individuals was 2.2% longer than that of social groups. Different strategies were observed for pedestrians to reach their destinations in the experiment. At the Prophase Free Movement stage, 98.8% of pedestrians walked straight to their destinations. In the Multidirectional Restricted Movement stage, 41% of pedestrians made detours to reduce their movement time, especially for individuals. In the movement with stopping, 83.3% of pedestrians preferred their initial strategy (straight strategy) and 16.7% of pedestrians changed their initial strategy to make detours and it could reduce their movement time. Our findings can be used to have a deep understanding on pedestrian dynamics and enhance the crowd management strategies.