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On current crowd management practices and the need for increased situation awareness, prediction, and intervention
Abstract
Recent accidents (News, 2006, 2010, 2013, 2015) show that crowded events can quickly turn into tragedies. The goal of crowd management is to avoid such accidents through careful planning and implementation. Crowd management practices are collaborative efforts between the different actors of the crowd management team and the crowd that depend on effective handling, sharing, and communication of information. Safety and comfort of a crowd depend on the success of such efforts. We have studied current practices and the role of technology through interviews to crowd managers. Our findings show that event planning and monitoring can be complex and sophisticated, but are operated with little support from technology. Crowd managers intend to increase their use of technology, but they have been so far dissatisfied by existing solutions. We provide recommendations for a bigger role of technology in crowd management.
... On the other hand, a multitude of crowd management research focuses on the actual management and programming (as opposed to the non-form-related) of the event while pointing out that inadequate emergency planning, insufficient training and experience with security, and understaffing are the primary and common attributes of crowd disasters [4,10,11,12] emphasize that 90% of crowd management is the planning before the event. The remaining 10% of the efforts focus on execution. ...
... The remaining 10% of the efforts focus on execution. In their work [12], understanding the crowd, location, and time of the event beforehand while cooperating and strategizing with multiple institutions and picking out on-site security personnel are all part of the pre-planning phase,executing the plan involved constant monitoring, controlling flows, preventing accidents, while ensuring communication throughout the event. ...
... Moreover, crowd management is a collaborative practice involving numerous stakeholders [12]. The stakeholders involved in a mass gathering event include both the attendees of the event and the staff responsible for their safety (police, firemen, medical services). ...
The demand for collective gatherings and recreational space has been rising as a response to the three-year itch of staying indoors and social distancing due to the global COVID-19 pandemic. Crowds are spilling out in the city and rejuvenating places, yet also causing dangerously overcrowded and uncontrolled situations. Planning is also challenged by the temporary use of space and the unpredictable nature of crowds during events, requiring a deep understanding of the crowd flow and flexibility in planning. How can places be prepared to accommodate the sudden influx of crowds safely? This chapter focuses on the case study of the tragic crowd crush in a once-festive alley celebrating Halloween in Itaewon, Seoul, the Republic of Korea, in 2022. What sort of planning methods could have prevented the crowd crush? Many past similar events worldwide reported a significant number of casualties and deaths, especially related to stampedes. This chapter analyzes the tragedy in retrospect in terms of form, programming, and the roles of stakeholders while reviewing and drawing lessons learned from relevant crowd-control measures elsewhere that could have been applied to avoid such an incident. Ultimately, this chapter offers insights and guidelines for planning for future crowd-raising occasions in limited spaces.
... Crowd management and crowd safety are both important aspects of event planning and management. The success of any event, whether it is a concert, sports game, festival, or political rally, depends on the ability of organisers to effectively manage the crowd, come up with timely and effective interventions and ensure their safety (Baxter et al., 2018;Earl et al., 2005;Emery, 2010;Martella et al., 2017;O'Toole, 2019;Wijermans et al., 2016). Crowd management is essential to prevent overcrowding, which can lead to accidents, injuries, and even fatalities (Abbott and Geddie, 2000;Hassanein et al., 2019). ...
... This is particularly the case when it comes to the deployment of Decision Support Systems (Martella et al., 2017;Van de Weghe et al., 2013;Wijermans et al., 2016) in crowd management practice. ...
... Authorities need to be vigilant in monitoring potential threats and suspicious activity around crowded spaces (Singh et al., 2020;Zhang et al., 2020a). The use of CCTV cameras, facial recognition technology, and other tools can aid in detecting potential threats, anomalies and identifying suspects (Feng et al., 2017;Mahadevan et al., 2010;Martella et al., 2017;Nishiyama, 2018;Sánchez et al., 2020;Yuan et al., 2014;Zitouni et al., 2016). Architects, event organisers and planners also need to incorporate security features such as bollards, barriers, and screening checkpoints into the design of crowded spaces (Chambers and Andrews, 2019;Ilum, 2022;Silberberg, 2013), while trying to preserve patron's mobility, accessibility and aesthetic satisfaction elements, especially with respect to the placement of bollards and protective barriers (Adams and Ward, 2020;Burns et al., 2021;Dorreboom and Barry, 2022;Tran et al., 2018). ...
... Responses to failed crowd management practices expand beyond simply increasing security staffing at events. There is a list of technologies that have already been deployed in managing crowds: surveillance cameras, heat maps, drones, prediction modeling software; yet these technologies only offer pre-event insights, provide limited coverage and information, or have further risked attendee safety (Brown et al. 2013;Hirth et al., 2021;Martella, 2017;Mowen et al., 2003;Solmaz et al., 2014). Advancements in crowd management practices are absolutely necessary to prevent additional event tragedies from occurring. ...
... Barriers are used to mold crowds or block off access to unauthorized areas. Barriers sometimes are disregarded or become dangerous blockades in mass evacuations and unexpected crowd volumes (Martella et al., 2017). Once crowding occurs; megaphones, audio systems, and static screen displays are used to send communications to attendees. ...
... In fact, a qualitative study with 55 crowd management experts found that effective crowd management takes place 90% pre-event and 10% during an event. Figure 1 illustrates the makeup of an effective crowd management plan (Martella et al., 2017). (Martella et al., 2017) Event Design and Communication are critical elements that can determine the success of a crowd management plan in events (Martella et al., 2017;Van Winkle & Bueddefeld, 2020). ...
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.
... Furthermore, crowds and personal space were also two major social density issues during anime conventions. These findings support the assertion that crowd management is crucial for event operators to avoid accidents and disasters (Martella et al., 2017). Large-scale conventions are mass gatherings and high-density events that carry a huge risk of widely spreading COVID-19, which has had an essentially negative impact on the performance of the event industry (Ryan et al., 2021;Seraphin, 2021). ...
... Crowd management is necessary for event operations to ensure the safety and security of the event and festival (Rahmat et al., 2011). Some possible considerations for crowd management include the greater use of technology such as self-service kiosks for check-in and crowd monitoring devices for tracking crowds (Martella et al., 2017). Event operators can hire crowd managers to evaluate the occupancy of the facility (Bigda, 2021). ...
Purpose
Guided by stimulus-organism-response (SOR) theory, this study analyzed the user-generated content (UGC) produced by attendees from six anime conventions in the USA.
Design/methodology/approach
A total of 739 online reviews and 1,932 photos were collected from the social platforms of six large anime conventions in the USA (Yelp and Facebook), and the study employed thematic analysis and image analysis to analyze the collected UGCs.
Findings
The findings revealed eight main themes (i.e. ambient and space, customers, service and products, sign and symbol, social density, emotional status, motivation, and behavior intention) and 32 subthemes across the three dimensions of SOR theory. Leveraging the power of cutting-edge image analysis, the image labels obtained from the analysis contributed to the creation of network clusters. The result of the image analysis also continued consistently with the thematic analysis result, which reflected SOR theory.
Research limitations/implications
Theoretically, the study applied SOR theory and blended thematic and image analyses to gain a comprehensive understanding of anime convention attendees’ experience and categorized the attendees’ emotional status as positive or negative to reflect their overall evaluation. Practically, this study highlighted some complaints from attendees and provided suggestions for operators. However, the study focused only on large anime conventions in the USA; future studies should compare attendees’ experiences with small and large conventions or anime conventions worldwide.
Originality/value
The study utilized UGCs to understand the key patterns essential to attendees during anime conventions in the USA and applied SOR theory to its investigation.
... Similarly, in another case study, Martella et al. [14] note that crowd management, with the help of technology, can reduce the volume of accidents in crowded events as well as ensuring the crowd's welfare. Their definition of crowd management indicates that crowd management is divided into two parts. ...
... The first one indicates that the analysis of crowd management, density of crowds, passenger flow and behaviour and other factors requires specific data to carry out the research, Kabalan et al. [10]. The other case study, by Martella et al. [14], highlights that the majority of crowd management efforts involve planning and preparation rather than the actual execution of an action. This means that in the design of a new railway station, ticket facilities such as the number of ticket gates and ticket machines need to be considered to ensure there is enough capacity for passengers inside the station, with minimal crowding and queuing. ...
During COVID-19, certain means were proposed to improve crowd management in the Birmingham New Street railway station. To validate the current system of crowd management in the station, this paper examines the rail passenger flow in the concourse of the Birmingham New Street railway station and the passenger interactions and queueing phenomena associated with it, mainly at the ticket machines, offices and gates, prior to and during the implementation of COVID-19 measures. The passenger behaviour in the concourse of the station was simulated using the SIMUL8 event-based simulation modelling package. Three different scenarios were modelled to analyse the changes and impacts from pre-COVID-19 and within the COVID-19 context. The results revealed that passenger behaviour in railway stations is changing due to COVID-19. Specifically, passengers are more likely to buy tickets using their smartphones or online prior to or whilst entering the station so that they can go through the station concourse with minimal queuing times and avoid contact with a facility of common use at the station, whereas those without tickets are more likely to be in a queue to buy their tickets in the station. For pre-COVID, the results showed that even with a reduced number of ticket machines, overcrowding inside the station was unlikely to occur, as 80% of all passengers in the simulation completed service within a 15-minute time frame. However, during implementation of COVID-19 measures, as the number of passengers using the station dropped significantly and more passengers bought their tickets using their smartphones and/or online, queueing times were also shorter, and thus passengers spent less time in the system. The simulation results were in accordance with the expected practice; hence the effectiveness of the simulation model was verified. Overall, as a result of this study, the following suggestions to improve crowd management in a railway passenger station concourse are proposed: encourage passengers to purchase tickets on their smartphones, remove ticket gates and replace them with sensors, and provide a one-way passenger flow system in the main concourse of the station.
... In the particular case of public events, event managers have expressed their interest in leveraging modern counting technologies to i) monitor events in real time [1,Sec. 7], ii) predict crowd counts in the future [1, Sec. ...
... and iii) perform post-analyses, to analyze the causes of overcrowding after its occurrence. In particular, computing real-time crowd densities in strategic areas allows security managers to decide whether an event has reached its maximum capacity [1], [2]. Crowd count time series can be fed into forecasting algorithms to predict overcrowding [3], [4]-which allows security personnel to execute countermeasures anticipatedly. ...
This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm -- as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.
... Martella Duives et al. defined a crowd as a group of people (N ≥ 10), who have gathered in a specific place, regardless of their language, nationality, sex or profession, whose movement is primarily dependent on local interactions (K ≥ 1 P/ m 2 ), and prolonged for a period of time (t ≥ 60 s) [11,12]. The density of crowds is a measure of the number of persons occupying the unit area. ...
... The benefits and limitations of each modeling approach were also highlighted. In recent years, multi-agent simulation has been widely used for crowd behavior analysis, crowd flow optimization, and management [11][12][13][14]. Kırlangıçoğlu et al. studied the feasibility of underground metro station using pedestrian simulation software [37]. ...
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.
... For the pair 'condition' and 'urban environment', the 'condition' characteristic indicating an indoor or outdoor environment is key information for crowd management (Martella et al. 2017). It is more important than a specific location as is indicated by the 'urban environment' characteristic. ...
... When applying these techniques in crowd management, crowd managers use the estimated crowd size following a two-phase process (Martella et al. 2017), i.e. planning phase and operational phase. In the planning phase, the historical event data, e.g. ...
City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.
... Understanding pedestrian dynamics [1,2] is critical for designing efficient urban spaces [3][4][5], managing large-scale events [6,7], and ensuring safety during emergencies [8][9][10]. ...
This study investigates the dynamics of pedestrian crossing flows with varying crossing angles to classify different scenarios and derive implications for crowd management. Probability density functions of four key featuresvelocity v, density , avoidance number Av, and intrusion number Inwere analyzed to characterize pedestrian behavior. Velocity-density fundamental diagrams were constructed for each and fitted with functional forms from existing literature. Classification attempts using Av-In and v- phase spaces revealed significant overlaps, highlighting the limitations of these metrics alone for scenario differentiation. To address this, machine learning models, including logistic regression and random forest, were employed using all four features. Results showed robust classification performance, with v and Av contributing most significantly. Insights from feature importance metrics and classification accuracy offer practical guidance for managing high-density crowds, optimizing pedestrian flow, and designing safer public spaces. These findings provide a data-driven framework for advancing pedestrian dynamics research.
... Tools for analysis of pedestrian flows are essential for the planning and geometric aspects of cities; design of infrastructure and addressing crowd safety concerns (Haghani, 2020). Without such insight, identifying the appropriate crowd and pedestrian response strategy remains ineffective (Martella et al., 2017). Therefore, predicting accurate spatio-temporal information about pedestrian activities such as volume, velocity and direction is fundamental to public safety and efficient urban planning (Zhang et al., 2022). ...
Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic utility of interconnected communities. The implementation of city-wide automatic pedestrian counting systems provides researchers with invaluable data, enabling the development and training of deep learning applications that offer better insights into traffic and crowd flows. Benefiting from real-world data provided by the City of Melbourne pedestrian counting system, this study presents a pedestrian flow prediction model, as an extension of Diffusion Convolutional Grated Recurrent Unit (DCGRU) with dynamic time warping, named DCGRU-DTW. This model captures the spatial dependencies of pedestrian flow through the diffusion process and the temporal dependency captured by Gated Recurrent Unit (GRU). Through extensive numerical experiments, we demonstrate that the proposed model outperforms the classic vector autoregressive model and the original DCGRU across multiple model accuracy metrics.
... During the COVID-19 pandemic, along with PPEs, people gathering detection and control was critical [43], [44]. The restriction for people gatherings is not only suggested for fighting the pandemic and safety in any environment: the presence of too many people can cause bottlenecks, and potentially block evacuation routes [45]. ...
The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.
... From this perspective, these models minimize assumptions about individual behaviors and concentrate more on grasping the mainstream dynamics of the crowd. On the other hand, crowd management can be defined as control strategies involving a cooperative endeavor among various members of the crowd management team and the crowd itself [3] . The effectiveness of these strategies relies on the efficient collection, distribution, and communication of information. ...
Crowd management is a significant topic especially for countries that support gathering events frequently. The Kingdom of Saudi Arabia hosts and manages one of the world class annual religious gatherings known as “pilgrimage”. Several challenges are raised for managing and controlling such mass gathering event. In this paper we propose a comprehensive framework for event processes modelling and management. The framework consists of four main stages starts with acquiring temporal data and ends by modelling different processes of the event. The main contribution of this work is to demonstrate how process mining techniques can be used innovatively to model the movement flow of crowd. Synthetic data is used to show a proof-of-concept of the proposed framework and the applicability of using it in modelling and monitoring real crowd movement scenarios.
... Crowd monitoring [1,[74][75][76][77][78][79][80][81][82][83] using Convolutional Neural Networks (CNNs) is a computer vision application that involves tracking and analyzing crowd behavior in real time using CNN-based algorithms. CNNs are a type of deep neural network that excels at processing visual data, such as images or videos, and can be used for crowd monitoring tasks to analyze various aspects of crowd behavior, such as crowd density, movement patterns, and anomaly detection. ...
This research paper presents a review of the use of convolutional neural networks (CNNs) for human crowd analysis. The paper discusses the challenges and limitations of methods and highlights the potential of CNNs in addressing these limitations. This study reveals and provides an in-depth analysis of the different techniques, architectures, and algorithms used in CNNs for human crowd analysis and their respective advantages and limitations. Additionally, the paper discusses the potential applications of CNNs in crowd analysis, including pedestrian detection, crowd counting, and crowd behavior recognition. The review also provides insights into the performance evaluation metrics commonly used in this area and the datasets used for training and testing CNNs. Overall, this review provides a comprehensive overview of the latest developments in the use of CNNs for human crowd analysis, as well as insights into future research directions in this field.
... Related research areas in the literature are crowd management, mainly for safety and disaster prevention, see e.g., [1]- [16], and for vehicle traffic control, see e.g., [17]- [22]. ...
... Depending on the requirements and characteristics of the problem/system, the quality threshold value can be fixed or varying over time. An example of ROOT Q is crowd monitoring and management [43] which can be classified as a dynamic covering location problem [44]. In this problem, the desirable locations of field agent units change over time based on the current and predicted status of the dynamic crowd. ...
Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions.
... Therefore, it is essential to increase the role of technology in crowd management. Through interviews with crowd managers and assessment of the current state of practice, research has shown that crowd monitoring and event planning are sophisticated, but operate with minimal technology support at present [4]. In addition, crowd managers prefer to increase their use of technology and seek improved tools to assist them in their work. ...
Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards.
... It also discusses the incidence of family groups, who form a group in public spaces, and the crowd density gets affected. The researchers Martella, Conrado and Vermeeran [5] suggested a method for crowd management in public events. Crowd management contains two different phases -preparation and execution. ...
Ever since the Covid-19 pandemic started, the world has seen many changes including the change in the lifestyle of people. To stop the spread of coronavirus, the major guidelines to be followed are wearing masks, sanitizing hands, and maintaining social distancing. The major problem is that most people fail to follow social distancing rules, which is flouted in public places. The need for a social distancing monitoring system has become one of the much-needed research areas in the present scenario such monitoring systems will alert the public when they refuse or fail to follow social distancing rules. In this paper we review the research carried out with respect to crowd management and social distance monitoring.
... A potential future work will be solving a real-world ROOT problem. An example of real-world ROOT problems is crowd monitoring and management [44] which is a dynamic covering location problem [45]. In this problem, the locations of security agent units are changed over time based on the status of the crowd. ...
Many real-world optimization problems are dynamic. The field of robust optimization over time (ROOT) deals with dynamic optimization problems in which frequent changes of the deployed solution are undesirable. This can be due to the high cost of switching the deployed solutions, the limitation of the needed resources to deploy such new solutions, and/or the system being intolerant towards frequent changes of the deployed solution. In the considered ROOT problems in this article, the main goal is to find solutions that maximize the average number of environments where they remain acceptable. In the state-of-the-art methods developed to tackle these problems, the decision makers/metrics used to select solutions for deployment mostly make simplifying assumptions about the problem instances. Besides, the current methods all use the population control components which have been originally designed for tracking the global optimum over time without taking any robustness considerations into account. In this paper, a multi-population ROOT method is proposed with two novel components: a robustness estimation component that estimates robustness of the promising regions, and a dual-mode computational resource allocation component to manage sub-populations by taking several factors, including robustness, into account. Our experimental results demonstrate the superiority of the proposed method over other state-of-the-art approaches.
... Aside from individual crowd contributions, a few studies have looked into facilitating communication among crowd members to respond to and manage unexpected events. Providing people with communication channels can help them gain a broader view of the event they need to deal with (Perez and Zeadally, 2019), and better coordinate their efforts (Martella et al., 2017). Song et al. (2020) analyzed a total of twelve international case studies of crowdsourcing and natural disaster governance. ...
Critical, time-bounded, and high-stress tasks, like incident response, have often been solved by teams that are cohesive, adaptable, and prepared. Although a fair share of the literature has explored the effect of personality on various other types of teams and tasks, little is known about how it contributes to teamwork when teams of strangers have to cooperate ad-hoc, fast, and efficiently. This study explores the dynamics between 120 crowd participants paired into 60 virtual dyads and their collaboration outcome during the execution of a high-pressure, time-bound task. Results show that the personality trait of Openness to experience may impact team performance with teams with higher minimum levels of Openness more likely to defuse the bomb on time. An analysis of communication patterns suggests that winners made more use of action and response statements. The team role was linked to the individual's preference of certain communication patterns and related to their perception of the collaboration quality. Highly agreeable individuals seemed to cope better with losing, and individuals in teams heterogeneous in Conscientiousness seemed to feel better about collaboration quality. Our results also suggest there may be some impact of gender on performance. As this study was exploratory in nature, follow-on studies are needed to confirm these results. We discuss how these findings can help the development of AI systems to aid the formation and support of crowdsourced remote emergency teams.
... Many an event organizer deals with crowd monitoring and management [1]. Recently, works from different teams proposed crowd counting systems using WiFi signals [2][3][4]. ...
Research has shown that counting WiFi packets called probe requests (PRs) implicitly provides a proxy for the number of people in an area. In this paper, we discuss a crowd counting system involving WiFi sensors detecting PRs over the air, then extracting and anonymizing their media access control (MAC) addresses using a hash-based approach. This paper discusses an anonymization procedure and shows time-synchronization inaccuracies among sensors and hashing collision rates to be low enough to prevent anonymization from interfering with counting algorithms. In particular, we derive an approximation of the collision rate of uniformly distributed identifiers, with analytical error bounds.
... Crowd management is a collaborative practice. The successful management of a mass gathering event depends on the cooperation and communication between all stakeholders [10]. ...
The Kumbh Mela is the largest religious and spiritual mass gatherings on the Earth. In this way, it remains a source of fascination for vast numbers of Hindus throughout the world. Around 240 million pilgrims participated during Kumbh Mela 2019. The Crowd management and the strategy for Security and surveillance have become a big challenge for such huge gatherings. This paper tries to find out the various risk factors and its management. It examines the role and responsibilities of various stakeholders in crowd management. Despite of some difficulties like lack of knowledge of number of people, crowd psychology and its behaviour pattern, this paper provides a comprehensive approach for risk analysis, preparedness, management and mitigation. The purpose is to make spiritual mass gathering events incidence free and enhance user experience by applying design thinking approach. Although this paper tried to cover all the aspects of crowd management and strategies for security and surveillance during mass gathering events, still many more approaches are there which can be further explored. The mega tent city that accommodates nearly millions of pilgrims in the river bed is significant not only for India, but also for mass gathering research at international level to draw policy. This provides the multifunctional issues to study the mega crowd events. This provides the opportunity to generate the field level evidence and document base for disaster management.
... Developing intelligent evacuation guidance systems is a major requirement for the correction of evacuees' decision-making behavior, which can support safe and efficient evacuation under emergency conditions [1][2][3][4]. Computational technology plays a significant role in the design of intelligent evacuation systems [4,5]. It is responsible for investigating dynamic environmental changes that result from cases of emergency, such as the presence of dangerous sources and the prevalence of their threat. ...
Understanding evacuees’ responses to dynamic environmental changes, during an emergency evacuation, is of great importance in determining which aspects are ideal and which aspects should be eliminated or corrected. Evacuees differ in their ability to continually plan escape routes and adapt the routes chosen when they become unsafe owing to moving sources of threat. This is because they have different views and perspectives. The perspectives of evacuees are stochastic and are characterized by a high degree of uncertainty and complexity. To reduce the complexity and control of uncertainty, a model is proposed that can test for variant stochastic representations of evacuees’ perspectives. Two extremely realistic perspectives—the most ideal and the least ideal—are proposed to reasonably limit the range of variance. The success of achieving optimal evacuation is tested when different tendencies towards extreme perspectives are adopted. It is concluded that data toward the most ideal perspectives are capable of demonstrating safer evacuation by reducing the number of simulated burnt agents. This study enables crowd managers and fire safety researchers to test guidance systems as well as configuration of buildings using different perspectives of evacuees.
... In light of the increasing size and frequency of mass events in public places [1] , ignoring the existence of crowd safety is becoming extremely difficult [2,3] . Effective crowd management [4] and evacuation schemes [5] are essential to reduce casualties and property loss during accidents [6] . For the purpose of revealing the underlying mechanism [7] of crowd motion in real life, many models [8] of pedestrian behavior have been proposed. ...
With the increase in large-scale incidents in real life, crowd evacuation plays a pivotal role in ensuring the safety of human crowds during emergency situations. The behavior patterns of crowds are well rendered by existing crowd dynamics models. However, most related studies ignore the information perception of pedestrians. To overcome this issue, we develop a visual information based social force model to simulate the interpretable evacuation process from the perspective of visual perception. Numerical experiments indicate that the evacuation efficiency and decision-making ability promote rapidly within a small range with the increase in unbalanced prior knowledge. The propagation of acceleration behavior caused by emergencies is asymmetric due to the anisotropy of visual information. Therefore, this model effectively characterizes the effect of visual information on crowd evacuation and provides new insights into the information perception of individuals in complex scenarios.
... The literature about crowd behavior centers on theoretical modeling of the crowd psychology [17] foretelling their behavior models inspired by physics, accepting behavior by different kinds of analysis [18]. Authors in [19] synthesize the prediction models of crowd behavior by studying the behavior of crowd. With the help of simulation these models are also used for planning the events prior to it. ...
... On the other hand, crowd management is proactively setting measures to prevent unforeseen circumstances from taking place. This includes proper resource allocation while stressing the significance of monitoring and prediction (Martella et al. 2017 (Gall et al. 2011) to a degree of success. The system developed by Viola et al. (2003) feeds two consecutive frames to a detector to analyze appearance and pattern information in order to detect walking individuals. ...
Developments on crowd counting and estimation are on the rise due to the emerging demand brought about by population increase and corresponding improvements in public safety planning. Various methods are successful in addressing challenges and limitations, such as occlusions and wide density variations. However, none have tackled the risky dynamic scenario of the annual Black Nazarene Procession in Manila City, Philippines. Extreme densities are reached as participants follow a moving subject. Yearly reported crowd estimates vary greatly as estimation methods used for this event remain undisclosed, undefined, or unpublished. Considering the strengths of both detection-based and regression-based crowd counting methods, a novel pedestrian estimation method is proposed to appropriately provide an accurate pedestrian estimate. Using video graphics, a static grid analysis is performed to systematically capture and evaluate actual participant density. From the recorded pedestrian densities, functions were developed to estimate densities at varying distances ahead and behind the carriage. A pedestrian joining density was established to account for the devotees who merge with the crowd way ahead of the procession. The 2019 event involved a moving carriage faithfully followed by thousands of devotees as it traveled along a 6.94-km route for 21.35 h. Employing a 95% confidence interval for the function intercepts and coefficients, an estimated range of 176,086-484,215 active pedestrian devotees during the procession was obtained using the developed systematic pedestrian estimation method. The social value of a more accurate crowd estimation method lies in providing policymakers with reliable crowd estimates that will enable the authorities to deploy the proper amount of security personnel for crowd management, as well as medical staff for emergency situations during mass gatherings of similar nature to the Black Nazarene Procession.
... Some have suggested exploring social media to reveal the psychological and emotional aspects of individuals in the crowd. Some have suggested using wearable sensors for automatic detection as well, but they are expensive compared to other methods [24]. ...
Hajj is an annual pilgrimage to Makkah in Saudi Arabia where over two and a half million pilgrims attend. It is one of the most intense crowded events in the world. Unfortunately, Hajj has witnessed several stampedes, fires and other disasters, which have led to the death of thousands of people. In recent years, the hajj management has made several improvements in the infrastructure, which has helped ease congestion in crowded rituals. However, despite the great development and huge contributions in improving the infrastructure of the facilities, the problem of crowd control and congestion still remains a real challenge to the management. Although crowd management during hajj is exemplary, it still remains largely manual. This research proposes an innovative solution by designing digital smart streets based on cheap LED-light screens, and control algorithm in addition to distributed fog nodes, wireless network sensors, and servers for the main computing and management. The proposed system will facilitate the process of controlling crowds, and the way it will function with the help of special signals and colors. These signs and instructions can be quickly and easily followed and adopted by people within the crowd. In this way, crowd management would be able to provide a prompt response to the requests and alerts from the central command. We use many techniques to detect the issues of crowds and locate places of interest based on the fog nodes and digital street processing. A prototype of the proposed system has been simulated to demonstrate its feasibility and ease of application to determine the benefits and features that it is capable to achieve, if implemented in crowded areas during the course of the Hajj.
This case study examines the crisis that transpired during the AstroWorld Festival in Houston Texas in November of 2021. Leveraging the tenets of concert management, crisis management and herd mentality, this case highlights the myriad planning and prevention mistakes that resulted in the deaths of 10 people and injuries to countless more due to crowd crush. A thoughtful analysis of the issues identifies sparse and improperly trained security, crowds exceeding the planned for capacity, a lack of crowdsourcing protocol, and improperly prepared performing artists as the perfect storm that resulted in a deadly event. The case culminates with asking students to contemplate better preventative and reactive practices and create protocols that could help prevent issues like the one at the AstroWorld Festival from occurring in the future.
Since more and more events are drawing large crowds, crowd management has become a crucial subject. The abstract explores the intricate area of crowd control, looking at the challenges faced by event organizers and the evolving tactics employed to ensure both public safety and the greatest possible experience for guests. A deep understanding of the challenges and the use of innovative solutions are crucial in the dynamic field of crowd management. Planning a large-scale event effectively necessitates an awareness of how technology, security, and human behavior interact. As events expand in size and complexity, crowd management strategies must be continuously improved to ensure the safety, security, and enjoyment of participants. With this abstract, a thorough analysis of the intricacies and advancements in the subject of crowd management might be started. The number of people attending public meetings has increased, and metropolitan crowd densities are continuing to climb, making crowd control more challenging than ever. Each year, many people die as a result of poor crowd control and planning. Crowd management is an interdisciplinary discipline that requires a grasp of technical and technological issues since crowd behavior and flow management entail both psychological and social elements. A wide range of crowd management best practices and challenges for effective systems are covered in this article.
Background The issue of crowd crushes has been not only very complicated but also uncertain. This article aimed to evaluate how situations such as the Itaewon Halloween crowd crush in South Korea in 2022 can be better managed to reduce human loss. Methods Qualitative analysis was the key methodology used to compare emergency planning for ordinary events with contingency planning for special events, focusing on four stakeholders, namely governments, businesses, voluntary organizations, and other local communities. Results The key finding was that all stakeholders would need to supplement emergency planning for ordinary events with contingency planning for special events for the nation. They must embody cooperation, cutting-edge technologies, routinized updates, situation awareness, political rationality, training and exercise, and others, based on inclusion. Conclusions This is a pioneer study that examined the Itaewon crowd crush more comprehensively than others in particular by including many disaster management principles.
Mass gatherings are events which can be spontaneous, planned sporting events or religious pilgrimages that attract people from all over the world. Kumbh Mela is the biggest divine and devout assembly on the earth. This chapter provides a holistic approach to pilgrimage itineraries dimensions which include crowd control on pilgrimage routes, pilgrimage experience, advantages, economic dimensions and safety and security of pilgrims etc. It highlights how the better transport facility, hospitality management, infrastructural development, world class amenities, proper planning and management etc. changes the pilgrims experience. The objective is to create mass gathering events free from occurrences. Although this chapter has attempted to cover various aspects of pilgrimage itineraries, still there are many other approaches that can be explored further. Hosting almost millions of pilgrims in the riverbed during Kumbh Mela is important not only for India but also for bringing together scientific forces at the international level for policy making.
This research aims to measure visitors’ satisfaction and investigate how crowd management plans and service quality affects visitors’ satisfaction in the Riyadh Season mega-event, To understand how visitors’ experiences at the Riyadh Season affect their return intentions, To examine how health and safety practices affect visitors’ experience and satisfaction at the Riyadh Season. Whereas the events that attract many people necessitate the organizers to have a crowd management plan and robust safety procedures to minimize possible risks. Whether sporting events, celebrity events, or major festivals, many people gathered in one place can have fatal consequences if not properly planned and controlled. Crowd management is essential for tourist attraction sites, especially for destinations that attract many visitors. A cross sectional survey approach was used to gather information and achieve the study purpose. Survey questionnaire was distributed to 542 participants through Google survey. Quantitative analysis using SPSS was performed on the data collected. The findings showed that service quality positively impact customer satisfaction. Similarly, quality health and safety measures enhance customer experience and satisfaction. The result also found that satisfaction with Riyadh season events increased their return intentions.
Background: The issue of crowd crushes has been not only very complicated but also uncertain. This article aimed to evaluate how situations such as the Itaewon Halloween crowd crush in South Korea in 2022 can be better managed to reduce human loss. Methods: Qualitative analysis was the key methodology used to compare emergency planning for ordinary events with contingency planning for special events, focusing on four stakeholders, namely governments, businesses, voluntary organizations, and other local communities. Results: The key finding was that all stakeholders would need to supplement emergency planning for ordinary events with contingency planning for special events for the nation. They must embody cooperation, cutting-edge technologies, routinized updates, situation awareness, political rationality, training and exercise, and others, based on inclusion. Conclusions: This is a pioneer study that examined the Itaewon crowd crush more comprehensively than others in particular by including many disaster management principles.
Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.
COVID-19 has posed a massive challenge for destination marketers to restore safety perceptions among tourists after the onslaught of the pandemic. The objective of the current study is to develop an integrated model linking social and technical elements via socio-technical theory helping in theory development and tourism recovery. Destination marketing researchers agree on the importance of STTs to restore faith among tourists However, there is a lack of theory in the studies related to tourism recovery. Socio-technical theory, which attributes the interdependence of technical systems and social systems for maximizing organizational productivity is used by the current study to help understand the tourism recovery process. This study uses the elements of socio-technical theory in a structural equation model (SEM) to help destination marketers better understand the effect of technology in attaining tourism recovery. Based on self-selection sampling, we collected the data via a web survey from makemytrip.com, the modeling in this study was done via variance-based structural equation modeling. The study results demonstrate a strong effect of Smart Tourism Technologies (STTs) in reducing the impact of pandemics on tourists’ perceptions.
With the increase of large-scale mass activities, stampedes of deaths and injuries easily occur. The bidirectional qualities between the state's shifting combinations of different risk factors and accidents must be more thoroughly analyzed. The triangular fuzzy number and the Bayesian network are applied in the study to construct a risk analysis model for stadium stampede accidents. 46 accident samples and 24 risk factors closely related to activities were collected for the study. The results show that 7 factors, including audience mood, vulnerable groups, retrograde crowd, abnormal person, flammable and explosive material, walkway in stadium, and special time period, are more likely to cause stampede incidents. When the factors related to the live audience are added together in the most dangerous state, the probability value of the activity safe state decreases from 86% to 31%. In particular, the mood state of the audience, ranging from the more excited to crazy, is most sensitive to stampede. In addition, four major causal chains are obtained. The two risk factors, crowd flow limitation measures and safe entrance and exit, contributed most significantly to the stampede when the personnel volume ratio began to exceed 80% or when the security entrances and exits were partially closed. The model constructed in this paper can dynamically demonstrate the rules between the transition combinations of different states of risk factors and stampede. It will provide some theoretical basis for the process safety management of large-scale mass activities in stadiums.
Analyzing pedestrian dynamics has since long been an active and practical field of interest. Since the introduction of, in particular, smartphones, various organizations saw a simple means for automatically measuring pedestrian dynamics. The basic idea is simple: network packets sent by WiFi-enabled devices can be collected by sensors and by extracting the unique MAC address from each packet, it should be possible to count how many devices are detected by a single sensor, as well as how devices move between sensors. Although this approach has been commercially deployed for many years, it is now largely forbidden (at least in the EU) due to obvious privacy infringements. In this paper, we address challenges and some potential solutions to automated measurement of pedestrian movements while protecting privacy. The results come from learning the hard way: having run experiments extensively over the past years, we have gradually gained considerable insight in what is possible and what may lie ahead.
Crowd management refers to the proactive preparation to avoid potential threats followed by the execution of that preparation to maintain the crowd’s safety during the event. The total effort to manage crowds is divided into 90% preparation and 10% execution, with the overall aim being to avoid the need for crowd control, which is a reactive approach. At sporting events, attendees’ perception of risk regarding their subjective safety is typically mild during these events because they typically perceive crowding as a more exciting atmosphere. In sport management research, spectators’ crowding perception has been considered complex as aesthetic and functional. Sport events naturally want bigger crowds for their organizations and fans. Safety and crowd management are still essential because people may avoid attending sport events unless safety reaches a certain perception level.
Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%.
Doi: 10.28991/esj-2021-SPER-14
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In the information age, activities and business models have become different. Factors such as new technologies and social media influence consumer behavior due to the variety of choices/channels. Companies are making a difference in consumers' minds by connecting with them active and creatively. Also, customers contribute with ideas and experiences, not only for products and services, but also for platforms and brands. Cyber-physical apps together with mobile media tend to revolutionize business models across multiple industries. This is important for tourism due to a contribution to link some still weakly linked activities across tourism sub-sectors in Portugal. The present work reflects on how Portugal is in terms of internet of things adoption (IoT) and digital transformation facing the challenges of sustainable and smart cities. It also explores which impacts this transformation can have through its platforms and processes in tourism and future services.
Die Umwälzungen im Nahen Osten, der sogenannte „Arabische Frühling“, wurden vermehrt in der akademischen Welt diskutiert. In diesem Kapitel wird gezeigt, wie die Wissenschaft mit dem Thema soziale Medien in Konfliktsituationen (mit Fokus auf den Nahen Osten) umgeht.
In Katastrophen und in humanitären Notlagen benutzen davon betroffene Personen aktiv
mobile Technologien. Dabei werden große Datenmengen generiert, die für die Hilfsorganisationen wichtige Informationen enthalten können. Das können z. B. Informationen über Art und Umfang der Katastrophe oder Hilfeersuchen von Betroffenen sein. Die Auswertung der Daten und die anschließende Bereitstellung der Ergebnisse kann durch digitale Freiwillige in der humanitären Hilfe, allen voran durch Organisationen des Digital Humanitarian Networks (DHN) oder Virtual Operations Support Teams (VOST), erfolgen. Diese Art der digitalen organisatorischen Strukturierung ermöglicht neue Formen des Engagements, die vor allem bei den Einsatzorganisationen aber auch Skepsis und Misstrauen erzeugen können. Wie zahlreiche Beispiele verdeutlichen, können Mittlerorganisationen oder -personen diese jedoch abbauen.
Für eine sichere Veranstaltung sind vier Einflussbereiche „Anlass und Art der Veranstaltung“ „Umwelt und Infrastruktur“, „Risiken und Gefahren“ sowie „Menschen“ im Zusammenhang und über die Phasen einer Veranstaltung hinweg zu betrachten, auch in Sicherheitskonzepten. Eventmanagement und Besuchersicherheit brauchen Wissen über Menschen, ihre Bedürfnisse und Verhaltensweisen, um Veranstaltungen besser an BesucherInnen anzupassen. Dies gilt für präventive (Crowd Management) wie für restriktivere Maßnahmen (Crowd Control) der Besuchersicherheit. Die Veranstaltungspraxis kann Erkenntnisse aus Human Factors-Psychologie und Sozialwissenschaften für das Wohlergehen und die Sicherheit der BesucherInnen nutzen und sie sogar als Sicherheitsakteure einbinden. Dazu werden in diesem Beitrag Themen wie Gruppen, Stimmung und Verhalten von Menschenmengen vorgestellt und zugehörige Theorien (z.B. soziale Identität) sowie Erkenntnisse zur psychologischen Dichte (Crowding) und zu Informationsbedürfnissen von BesucherInnen.
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 inCrowd, 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.
Emotion detection plays a vital role in crowd management as it enables social event organizers to detect the actions of masses and react accordingly. There are several approaches to detect emotions in a crowd, including surveillance cameras, human observers and sensors. One other approach to gather emotion data is self-reporting. A recent study showed that self-reporting is feasible, reliable and efficient. However , there is a strong privacy concern among people that risks the use of such self-reporting mechanisms in wide use. In this work, we address the privacy aspect of self-reporting mechanism and propose a cryptographic approach that hides the sensitive data from the organizers but permits to compute statistical data for crowd management. The feasibility of using cryptography in real life for privacy protection is also investigated in terms of complexity.
We address the problem of estimating the neighborhood cardinality of nodes in dynamic wireless networks. Different from previous studies, we consider networks with high densities (a hundred neighbors per node) and where all nodes estimate cardinality concurrently. Performing concurrent estimations on dense mobile networks is hard; we need estimators that are not only accurate, but also fast, asynchronous (due to mobility) and lightweight (due to concurrency and high density). To cope with these requirements, we propose Estreme, a neighborhood cardinality estimator with extremely low overhead that leverages the rendezvous time of low-power medium access control (MAC) protocols. We implemented Estreme on the Contiki OS and show a significant improvement over the state-of-the-art. With Estreme, 100 nodes can concurrently estimate their neighborhood cardinality with an error of ≈10%. State-of-the-art solutions provide a similar accuracy, but on networks consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.
There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we introduce and describe mathematical methods based on pedestrian-behavior models to infer and visualize crowd conditions from pedestrians' GPS location traces. We tested our approach during the 2011 Lord Mayor's Show in London by deploying a system able to infer and visualize in real-time crowd density, crowd turbulence, crowd velocity and crowd pressure. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device's location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. As an evaluation of the usefulness of our approach, we learned through interviews with police officers that our approach helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
We are only starting to understand how people behave when they are part of a crowd. This article presents a novel approach to the study and management of crowds. The approach comprises a device to be worn by individuals, an infrastructure to collect the information from the devices, a set of algorithms for recognizing crowd dynamics, and a set of feedback strategies to intervene in the crowd. A fundamental element of our approach is to consider crowds in terms of their texture. The crowd texture is represented through the proximity graph, a data structure that captures the spatial closeness relationship between individuals over time. We address its properties and limitations, a system architecture to measure and process it, and a few examples of insights that can be obtained from analyzing it.
Large-scale festivals with a multitude of stages, food stands, and attractions require a complex perimeter design and program planning in order to manage the mobility of crowds as a controlled process. Errors in the planning phase can cause unexpected crowd dynamics and lead to stampedes with lethal consequences. We deployed an official app for Zu ̈ri Fa ̈scht 2013 -- the largest Swiss event -- over a period of three days. The app offered information about the festival and featured a background localization allowing us to collect continuously the visitor position. With 56.000 app downloads and 28.000 user contributing 25M location updates in total, we obtained a large scale dataset. By aggregation of location points it captures complex crowd dynamics during the entire festival. In this paper we present the data collection for Zu ̈ri Fa ̈scht 2013 and best practises to acquire as many contributing users as possible for such an event. We show the potential of aggregating individual location data and visualising relevant parameters that can serve as tool for analysis and planning of program and perimeter design.
This paper introduces the concept of crowd well-being and the needs for sustaining it. Crowd well-being can be interpreted as crowd members’ evaluations on their emotional reactions, moods and judgments they form about their satisfactions, goals or needs fulfillment in a crowded situation according to the definition of subjective well-being by Diener and his colleagues. Higher level needs, i.e. relatedness and autonomy in Sheldon’s two-level hierarchy are essential to the enhancement of crowd well-being when safety is guaranteed. Needs for safety become prominent in case of emergencies. Furthermore, it investigates three representative crowd management designs and strategies and how they fulfill the needs of crowd members through ten crowd expert interviews. We conclude that the current strategies and designs mostly focus on the effortful planning and preparation for the potentially unsafe situations, which tend to be coercive instead of fully respect crowd members’ autonomy. A number of technologies are applied to monitor the crowd behaviors externally due to the security concerns rather than approach and assess them locally, understand their needs and provide real-time feedbacks to support their well-being. We suggest that future designs should allow the measuring to go into the crowds and place an emphasis on improving crowd members’ higher level needs on the premise of security.
Particularly since the 11 September terrorist attacks in the USA, much attention has been given to the development and implementation of incident management systems (IMS). The IMS is a tool for marshalling pre-identified and pre-assembled resources to respond to an emergency or disaster. IMS is particularly useful when personnel and resources from many agencies and jurisdictions are required to manage large incidents successfully. While many IMS have been devised over the years, their use remains intermittent. This paper traces the evolution of IMS, reviews how it can be integrated into jurisdictional emergency and disaster management, and specifies the structures that are used in most incident management systems at the municipal level.
Events are part of a booming industry that continues to grow both domestically and internationally. This increase in popularity has lead to larger and more diverse attendees, making crowd management and crowd control a necessary and integral part of the planning process for any event. This study explores the significance of crowd management and crowd control. It discusses the need for proper operating procedures and the impact of inadequate crowd managment and control. The study also provides and analysis of recent legal opinions depicting both adequate and inadequate crowd management techniques.
Decision-making in emergencies requires non-traditional approach and tools characterized by non-hierarchical structure and flexibility. The dynamic environment of disasters makes it imperative to invest in inter-sector and inter-agency cooperation and coordination. Focusing on the Emergency Management Assistance Compact's (EMAC) response to Hurricanes Katrina and Rita in 2005, this article examines the decision-making structure of the agreement. EMAC is an inter-state mutual aid agreement that facilitates sharing of resources during and after disasters. While EMAC's overall decision-making performance was relatively satisfactory and flawless, investment in communication, trust-building, and eradication of inter-agency value differences and discrepancies is imperative.
We present a socio-technical systems framework and underlying principles to help understand a sample of crowd-related disasters. Our approach is founded on the premise that disasters result from complex systems failures, wherein a series of interdependent factors combine in such a way as to cause problems. We explore the explanatory power of our approach by analysing three incidents; Hillsborough football stadium disaster (1989), King's Cross underground fire (1987), and Bradford City stadium fire (1985). We find a common set of fundamental, interrelated issues and consistent violations of our socio-technical design principles. We conclude by discussing how our framework, principles and socio-technical thinking more generally, may contribute to theory and practice.
Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms of their attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a one-hour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intention-usage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models of the determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology.
We envision a sensor-enabled mobile system to assist organizers and participants of public events in emergencies and evacuation situations using human computing principles. By autonomously detecting elements of emergent collective behavior (e.g. clogging, queuing, herding), efficient interaction and information sharing with specific groups in the crowd is enabled. We have previously shown the technological feasibility to infer collective behavior patterns from mobile sensor data. In this paper we report on key user aspects influencing further investigations: the perceived usefulness of this technology; the desired system deployment vector; and privacy considerations. Based on answers of 92 attendees at a city-wide public event we found that mobile phones were the preferred vector for mobile emergency assistants. We confirmed the acceptance of sharing privacy sensitive context information in this limited application purpose. Additionally, we report statistics on the desired feature sets of a mobile service to augment penetration of such emergency technology by adding value for non-emergency use.
Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.
This paper presents the developed concept of management and control to the pedestrian flow movements at Jamarat in the regular annual pilgrimage to Makkah (Hajj) season in Saudi Arabia. Every year, 3 to 4 millions of pilgrims perform their rituals in the course of extensively high restrictions, in the midst of a climax of limited/narrow space and time constraint. The Jamarat where pilgrims gather to perform a ritual stoning of pillars symbolizing the devil as part of the Hajj. The new Jamarat leveled building replaced the old ones. The project objective is to prevent crowd panic and to minimize the risk of crowd disasters. Management and control of pedestrian group movement to and/or inside Jamarat leveled building and area, using new experimental knowledge methodologies observed from the science of crowd dynamics, throughout anticipation and analysis of the pilgrim flow from low crowd density to extremely high crowd density, accompanied, attended, and escorted with an insider real-time-life video- scrutiny/observation/analysis.
There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we present a framework to infer and visualize crowd behavior patterns in real-time from pedestrians’ GPS location traces. We deployed and tested our framework during the 2011 Lord Mayor’s Show in London. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device’s location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. We learned from the police officers that our framework helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
This paper presents a scenario-based approach to deal with uncertainties in situation assessment problems. Scenario representation is based on causal models, whereas scenario generation involves the estimation of the states of model variables, done by means of observations and inferences of hidden states by using domain knowledge. Moreover, scenario management is addressed by means of a probabilistic framework involving Bayesian and credal networks, which allows the evaluation and ranking of scenarios according to likelihood, used to prioritize information to be presented to decision makers. The presented scenario approach also supports the adaptation of the reasoning models on the fly, as scenarios are generated and relevant information changes or becomes available.
Crowd dynamic management research has seen significant attention in recent years in research and industry in an attempt to improve safety level and management of large scale events and in large public places such as stadiums, theatres, railway stations, subways and other places where high flow of people at high densities is expected. Failure to detect the crowd behaviour at the right time could lead to unnecessary injuries and fatalities. Over the past decades there have been many incidents of crowd which caused major injuries and fatalities and lead to physical damages. Examples of crowd disasters occurred in past decades include the tragedy of Hillsborough football stadium at Sheffield where at least 93 football supporters have been killed and 400 injured in 1989 in Britain's worst-ever sporting disaster (BBC, 1989). Recently in Cambodia a pedestrians stampede during the Water Festival celebration resulted in 345 deaths and 400 injuries (BBC, 2010) and in 2011 at least 16 people were killed and 50 others were injured in a stampede in the northern Indian town of Haridwar (BBC, 2011). Such disasters could be avoided or losses reduced by using different technologies. Crowd simulation models have been found effective in the prediction of potential crowd hazards in critical situations and thus help in reducing fatalities. However, there is a need to combine the advancement in simulation with real time crowd characterisation such as the estimation of real time density in order to provide accurate prognosis in crowd behaviour and enhance crowd management and safety, particularly in mega event such as the Hajj. This paper addresses the use of novel sensory technology in order to estimate people's dynamic density during one of the Hajj activities. The ultimate goal is that real time accurate estimation of density in different areas within the crowd could help to improve the decision making process and provide more accurate prediction of the crowd dynamics. This paper investigates the use of infrared and visual cameras supported by auxiliary sensors and artificial intelligence to evaluate the accuracy in estimating crowd density in an open space during Muslims Pilgrimage to Makkah (Mecca).
This document is a review of the burgeoning literature on the utilisation of AmI (Ambient Intelligence) technology in two contexts: providing support and enhancing crowd evacuation during emergencies and improving traffic management.
A real-time understanding of the behavior of pedestrian crowds in physical spaces is important for crowd monitoring and management during large-scale mass gatherings. Thanks to the proliferation of location-aware smartphones in our society, we see a big potential in inferring crowd behavior patterns by tracking the location of attendees via their mobile phones. This chapter describes a framework to infer and visualize crowd behavior patterns in real-time, using a specially developed smartphone app. Attendees at an event voluntarily provide their location updates and in return may receive timely, targeted and personalized notifications directly from the security personnel which can be of help during an emergency situation. Users also have access to event-related information including travel advice to the location. We conducted a systems trial during the Lord Mayor’s Show 2011 in London, UK and the Notte Bianca festival 2011 in Valletta, Malta. In this chapter, besides verifying the technological feasibility, we report on interviews conducted with app users and police forces that were accessing the monitoring tools during the event. We learned from both sides that the created feedback loop between the attendees of the event running the app and the security personnel is seen as a strong incentive to follow such a participatory sensing approach. The researchers worked closely with policy makers, the emergency services and event organisers and policy implications of using the Socionical App will be discussed; as well as the response of users to being guided by an AmI device during a possible emergency.
This paper provides summaries of several ways of distinguishing and assessing crowds within the context of special event planning (Berlonghi, 1991, 1993). These “ways” are not a list of alternative methods or approaches, but rather closely related factors that must be considered for each and every event. Understanding crowds and crowd behaviour must not remain an academic exercise. Effective and appropriate application and implementation is critical!Those involved in crowd management and crowd control cannot be excused from the significant responsibility of providing the public with the highest standard of safety and security that is both possible and feasible. They must first foresee the nature of the crowd that will be in attendance. Secondly, they must be able to observe the behaviour of a crowd while an event is taking place and make timely decisions for effective action. Finally, they must have the ability to establish policies, design plans and execute operations taking into consideration the configuration of the venue and the set-up of the particular special event.
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, μ-Argus and k-Similar provide guarantees of privacy protection.
A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.
This chapter identifies a class of worksites characterizable in terms of participants' ongoing orientation to problems of space and time, involving the deployment of people and equipment across distances according either to a timetable or to the emergent requirements of a time-critical situation. To meet simultaneous requirements of mobility and control, centers of coordination must function as centers to which participants distributed in space can orient, and which at any given moment they know how to find. At the same time, to coordinate activities distributed in space and time personnel within the site must somehow have access to the situation of co-workers in other locations. One job of technologies in such settings is to meet these requirements through the reconfiguration of relevant spatial and temporal relations. This general characterization is explored through ethnographic materials from an investigation of the work of airline ground operations at a metropolitan airport on the west coast of the United States.
Currently, pedestrian simulation models are used to predict where, when and why hazardous high density crowd movements arise. However, it is questionable whether models developed for low density situations can be used to simulate high density crowd movements. The objective of this paper is to assess the existent pedestrian simulation models with respect to known crowd phenomena in order to ascertain whether these models can indeed be used for the simulation of high density crowds and to indicate any gaps in the field of pedestrian simulation modeling research.This paper provides a broad, but not exhaustive overview of the crowd motion simulation models of the last decades. It is argued that any model used for crowd simulation should be able to simulate most of the phenomena indicated in this paper. In the paper cellular automata, social force models, velocity-based models, continuum models, hybrid models, behavioral models and network models are discussed. The comparison shows that the models can roughly be divided into slow but highly precise microscopic modeling attempts and very fast but behaviorally questionable macroscopic modeling attempts. Both sets of models have their use, which is highly dependent on the application the model has originally been developed for. Yet, for practical applications, that need both precision and speed, the current pedestrian simulation models are inadequate.
This paper considers a number of factors affecting the flow of pedestrians, and reviews the available data for the appraisal of existing facilities and the design of new facilities. Concern is expressed about the application of existing standards to future design.
This article presents a selected literature review of the critical studies with an analysis of the development of the study area concerned with the behaviour of the occupants during a 5re occurrence. Although, the primary emphasis is on the developments in this study area within the United States from the early 1900s until 1998, the literature cited is of world wide origin. The in6uence of the performance code concept on the human behaviour in 5re research with the resulting emphasis on the evacuation models is examined. Cautions are expressed relative to the design, validation and application of the evacuation models. Concerns are expressed relative to the application of the evacuation models to the simulation of the documented behaviour of occupants in 5re incidents. Copyright ( 1999 John Wiley & Sons, Ltd.
This paper is a review of the author's 1976 "Principles of Sociotechnical Design." While most of the principles set out there have stood the test of time and experience, modifications are needed. In particular, the principles that govern the process of design and the activities of the design team are even more closely bound up with the principles governing the design itself. Some new principles are proposed. More attention is given to the needs of the organization as a society.
In the case of big events where hundreds or even thousands of people may gather together, task forces of police units, fire brigade units, medical corps units and so on are usually sent to the happening in order to ensure safety and help within short response time. Defining the number of required task force units and locating these task force units within the event area is a critical problem for the commanding decision maker. In this paper, we will first reveal how such decisions are usually made in practice today by reporting the result of interviews with practitioners who were in charge in such situations in Germany. Then we will provide mathematical models and report on computational studies to demonstrate how these decisions can be supported by operations research techniques. Finally, using data from a practical case in the city of Dresden where 50,000 people gathered together, we show that our models can indeed be used to solve real-world problems using commercial software.
Work undertaken to quantify the relationships between crowd velocities, flow rates and densities for uni-directional motion is reviewed. Most of the available data has been generated for underground stations in the UK; similar work for commuter stations in Japan is introduced and developed. Maximum observed flow rates from this work are compared with those suggested in the ‘Green Guide’ for the evacuation of sports grounds. The ‘Green Guide’ figures are higher than the maximum values obtained from the work reviewed.
The crowd flow pattern under emergency situations has been studied on the basis of dynamic movement principles. It has been demonstrated that the surrounding crowd density will influence the speed of an individual. The derivation of the movement equations of people with respect to the impacts at front, back and lateral directions has been given, and it shows that the impacts have substantial influence on the people's movement speed. The logarithmic relationship between the crowd density and the speed is in good agreement with the published field data. The study also demonstrates that the influence of the crowd movement velocity by the inter-person effect at the lateral direction is much lower than that at the front–back direction.
Socio-technical systems thinking has predominantly been applied to the domains of new technology and work design over the past 60 years. Whilst it has made an impact, we argue that we need to be braver, encouraging the approach to evolve and extend its reach. In particular, we need to: extend our conceptualization of what constitutes a system; apply our thinking to a much wider range of complex problems and global challenges; and engage in more predictive work. To illustrate our agenda in novel domains, we provide examples of socio-technical perspectives on the management of crowd events and environmental sustainability. We also outline a research and development agenda to take the area forward.
Simulation tools are often used to establish pedestrian and evacuee performance. The accuracy and reliability of such tools are dependent upon their ability to qualitatively and quantitatively capture the outcome of this performance; i.e. whether the simulated agents perform the expected acts and take the expected amount of time to complete them. This article investigates the relationship between simulating individual agent actions and generating reliable emergent, emergent conditions (e.g. congestion). Once this relationship is established for a particular tool, it can then be used to investigate the conditions that may emerge in certain scenarios and mitigate against them. This article presents a simple framework for categorising real-world observations and then translating these observations into the simulated environment – extracting key information from the data collected to configure the simulation tool as required. The article addresses the qualitative benefits of representing individual-level actions, and, to a lesser degree, the quantitative benefits, although this effort is limited given the nature of the data. It tests this relationship using observations made at the Hajj, specifically the Sa’ee where large numbers of pilgrims perform religious rites in concert. Several scenarios are simulated using the buildingEXODUS model, enabling the importance of individual-level behaviours upon emergent conditions to be investigated, even when simulating relatively large crowds of up to 15,000 people.
Computer based analysis of evacuation can be performed using one of three different approaches, namely optimization, simulation and risk assessment. Furthermore, within each approach different means of representing the enclosure, the population and the behaviour of the population are possible. The myriad of approaches that are available has led to the development of some 22 different evacuation models. This review attempts to describe each of the modelling approaches adopted and critically review the inherent capabilities of each approach. The review is based on available published literature. Copyright © 1999 John Wiley & Sons, Ltd.
This paper provides summaries of several ways of distinguishing and assessing crowds within the context of special event planning (Berlonghi, 1991, 1993). These “ways” are not a list of alternative methods or approaches, but rather closely related factors that must be considered for each and every event. Understanding crowds and crowd behaviour must not remain an academic exercise. Effective and appropriate application and implementation is critical!Those involved in crowd management and crowd control cannot be excused from the significant responsibility of providing the public with the highest standard of safety and security that is both possible and feasible. They must first foresee the nature of the crowd that will be in attendance. Secondly, they must be able to observe the behaviour of a crowd while an event is taking place and make timely decisions for effective action. Finally, they must have the ability to establish policies, design plans and execute operations taking into consideration the configuration of the venue and the set-up of the particular special event.
In this paper we describe the development of a decision support system for crowd control. Decision support is provided by suggesting a control strategy needed to control a specific current riot situation. Such control strategies consists of deployment of several police barriers with specific barrier positions and barrier strengths needed to control the riot. The optimal control strategy for the current situation is found by comparing the current situation with pre-stored example situations of different sizes. The control strategies are derived for these pre-stored example situations by using genetic algorithms where successive trial strategies are evaluated using stochastic agent-based simulation.
The division of labour, in its turn, implies interaction; for it consists not in the sheer difference of one man's kind of work from that another, but in the fact that the different tasks and accomplishments are parts of a whole to whose product all, in some degree, contribute. And wholes, in the human social realm as in the rest of the biological and in the physical realm, have their essence in interaction. Work as social interaction is the central theme of sociological and social psychological study of work.
With the maturity of sensing and pervasive computing techniques, extensive research is being carried out in using different sensing techniques for understanding human behaviour. An introduction to key modalities of pervasive sensing is presented. Behaviour modelling is then highlighted with a focus on probabilistic models. The survey discusses discriminative approaches as well as relevant work on behaviour pattern clustering and variability. The influence of interacting with people and objects in the environment is also discussed. Finally, challenges and new research opportunities are highlighted.
This paper argues that a comprehensive approach to crowd safety design, management and risk assessment needs to integrate psychology and engineering frames of reference. Psychology and engineering are characteristically mutually exclusive in their focus on the perspective of crowd members who think and behave (psychology) or on static and dynamic objects (engineering). Engineering places as much emphasis on the physical environment as psychology negates the relationship between the physical environment and people. This paper stresses the need to address the relationship between (A) design and engineering×(B) communications technology × (C) crowd management × (D) crowd behaviour and movement. Theories of crowd psychology are briefly reviewed with particular reference to crowd ingress and egress and misconceptions about ‘panic’ or irrational behaviour. Assumptions about panic reinforce an emphasis on the control of a crowd, as if a crowd is a homogeneous mass of bodies or ‘ballbearings’, rather than the management of a crowd as a collection of individuals and social groups who need accurate and timely information if they are to remain safe. Particular emphasis is put on the fact that the time for a crowd to escape from a situation of potential entrapment is a function of T (Time to escape) = t1 (time to start to move) + t2 (time to move to and pass through exits), rather than T = t2. This is illustrated by reference to research of escape behaviour in the Summerland fire and underground station evacuations. The paper concludes by stressing the need to validate computer simulations of crowd movement and escape behaviour against psychological as well as engineering criteria.
The article presents a methodological framework for the design of formative evacuation plans for complex socio-technical systems in crisis. The framework adopts the Complex Adaptive Systems modelling approach, and proposes the agent-based simulation as cognitive tool for the team which designs the plans. The formative evacuation plans, which are developed using the proposed framework, do not prescribe normative procedures but provide recommendations that decrease the problem space of the personnel, and at the same time permit them to adapt to the particularities of the situation at hand. The framework is demonstrated through an application in a metro system, for the case of a flaming train stalled between two stations.
Major accidents keep occurring that seem preventable and that have similar systemic causes. Too often, we fail to learn from the past and make inadequate changes in response to losses. Examining the assumptions and paradigms underlying safety engineering may help identify the problem. The assumptions questioned in this paper involve four different areas: definitions of safety and its relationship to reliability, accident causality models, retrospective vs. prospective analysis, and operator error. Alternatives based on systems thinking are proposed.
This paper proposes a mathematical model and a computational approach to study the complex multiphysical non-linear coupled system that results from the interaction between a moving platform and the pedestrians who walk on it. The described method is based on the mathematical and numerical decomposition of the coupled system into two subsystems and on the two-way interaction between them. In particular, the dynamics of the crowd is modelled referring to a macroscopic description in analogy to that of a compressible flow. The proposed approach is applied to the lateral vibrations of footbridge decks under human-induced excitation. First, the computational parameters of the model are optimized. Then, the effects of the crowd initial density and of the runnability conditions are evaluated on a motionless platform. Finally, the results obtained from the simulations of the crowd–structure interaction are commented on.
