Network redesign for efficient crowd flow and evacuation

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

IntroductionIn the past decade, India has witnessed many lapses in crowd safety during mass gatherings. The high casualty rate in stampedes during traditional mass gatherings has prompted the study of these events. Wide variations exist in casualty rates for similar events, and key issues in healthcare services in these special situations were addressed in the Indian context.MethodsFrom 2001–2010, Mass gathering data were collected from news items reported in the archives of newspapers, “The Times of India”, “The Hindu” and “The Indian Express”. The keywords used were: “stampede”, “mass gathering”, “mass-gathering events”, “mass-gathering incidents”, “crowd”, and “crowd management”. The study included triggers for the incident and the number of casualties (dead and injured) in each incident.ResultsIn 27 separate mass gatherings in India, there were 936 dead and 540 injured casualties. The unique characteristics of mass gatherings in India included a predominance of old and vulnerable people in traditional mass gatherings, in contrast to the young and middle-aged groups who gather for music and sporting events elsewhere. Further, alcohol/substance abuse, brawls, and violent behavior were absent at traditional Indian mass gatherings. Non-traditional mass gatherings accounted for a lesser number of incidents in India, and were located in movie theatres and railway stations.ConclusionsIn a populous country like India, traditional mass gatherings predominate, and ensuring the health, safety, and security of the public at such events will require an understanding of crowd behavior, critical crowd densities, and crowd capacities in the Indian context. However, planning for mass gatherings can be developed using the existing body of knowledge of mass-casualty preparedness, food safety, and health promotion.

The issue of safety at sports mega-events, with a particular focus on football matches, has emerged as a pivotal concern for policymakers, event organisers and international governing bodies. Whilst such events are held to symbolise unity and cultural exchange, they also present substantial legal, economic and organisational challenges. The present study investigates the multifaceted issue of stadium safety by examining its legal frameworks, economic ramifications, and evolving technological solutions, employing football as the primary case study. The present study adopts a multidisciplinary methodology. A comprehensive review of the extant literature, encompassing academic studies, regulatory guidelines and institutional reports, established the theoretical foundation. A review of historical case studies was conducted to extract lessons from past failures in crowd management and emergency response. The case studies included the Hillsborough, Heysel, and Luzhniki disasters. Comparative and economic analyses were employed to evaluate safety investments in various regions and contexts, focusing on FIFA World Cup evaluations and national strategies, including those of the Latvian Football Federation. Additionally, the contributions of international legal instruments, including the Saint-Denis Convention, the Balance S4 Initiative and the Green Guide, to safety, security and services in stadium environments were evaluated. Finally, technological projects such as eVACUATE were analysed to determine their innovative contributions to predictive crowd modelling and emergency coordination. The primary objective of this research endeavour is to investigate the potential for aligning legal regulations, economic considerations, and technological innovation to engender safer and more inclusive sports environments. A fundamental concept in the analysis is the development of a "security culture", encompassing awareness, collaboration, adaptability, technological integration, and cultural sensitivity. The study emphasises the necessity of embedding this culture into stadium management, thereby highlighting the importance of implementing both preventive measures and rapid responsiveness to evolving risks, including the emergence of threats such as cyberattacks. The findings show that, although safety investments require significant financial commitments, the long-term costs of neglect, measured in terms of human lives lost, reputational damage and legal liabilities, far exceed the costs of prevention. Lessons from past tragedies emphasise the importance of consistently enforcing safety frameworks and integrating best practices into national policies. Furthermore, technological advances in crowd monitoring, evacuation planning and data-driven decision-making offer the opportunity to enhance existing protocols. In conclusion, the study argues that achieving sustainable safety at major sporting events requires international co-operation, standardised legal frameworks and balanced economic strategies. Future policy should focus on reducing disparities between countries with different resources, promoting a global culture of safety and investing in innovation driven by research. Such measures will ensure that sporting events remain secure, inclusive and celebratory occasions.

Cellular Automata (CA) as bio-inspired parallel computational models of self-reproducing organisms can capture the essential features of systems where global behavior arises from the collective effect of simple components which interact locally. In this aspect, CAs have been considered as a fine candidate to model pedestrian behavior and crowd dynamics in a fine manner. In specific, for crowd modeling, the CA models show evidence of a macroscopic nature with microscopic extensions, i.e. they provide adequate details in the description of human behavior and interaction, whilst they retain the computational cost at low levels. In this paper several CA models for crowd evacuation taking into consideration different modeling principles, like potential fields techniques, obstacle avoidance, follow-the-leader principles, grouping theory, etc. will be presented in an attempt to accomplish efficient crowd evacuation simulation. Moreover, an integrated system based on CAs that operates as an anticipative crowd management tool in cases of medium density crowd evacuation for indoor and outdoor environments is also shown, and its results different real world cases and different environments prove its efficiency. Finally, robot guided evacuation with the help of CAs is also presented. Quite recently, an evacuation system was proposed, based on an accurate CA model capable of assessing the human behavior during emergency situations takes advantage of the simulation output to provide sufficient information to a mobile robotic guide, which in turn guides people towards a less congestive exit at a time.

Crowd management aims to develop support infrastructures that can effectively manage crowds at any time. In emergency and disruptive scenarios this concept can minimize the risk to human life and to the infrastructure. We propose the Communication Architecture for Crowd Management (CACROM), which can support crowd management under emergency and disruptive scenarios. We identify, describe, and discuss the various components of the proposed architecture, and we briefly discuss open challenges in the design of crowd management systems for emergency and disruptive scenarios.

Crowd evacuation with human-level intelligence via neuro-symbolic approach

During Hajj, Muslims throughout the globe assemble at the holy sites in Makkah, Saudi Arabia. The Saudi government aims to increase the number of pilgrims. To ensure the pilgrims’ safety from the impact of surface urban heat island (SUHI), a scientific approach using artificial intelligence and Earth observation (EO) is recommended for crowd management and human health. SUHI is usually measured using satellite LST data. UHIs impact the walkability of cities in hot climates. The development of digital technologies has provided researchers with a better understanding of crowd management modeling to control such a mass gathering, especially within the summer season and in drought regions. In this study, an approach was used to detect the UHI in holy sites and understand the factors causing them. To achieve this goal, EO data were used to calculate the LST using the Landsat 8 thermal band (TIRS) and calculating the surface emissivity and Normalized Difference Vegetation Index (NDVI). Then, UHIs were identified by adding the mean of the LST to half of its standard deviation. Based on the results of this study, LST had a strong correlation with NDVI (negative) in Arafah. In addition, the strength of the correlation became much weaker within Mina and Muzdalifah. As for the correlation of LST and elevation, the strength appeared to be weak but negative in Arafah, but stronger in Muzdalifah and Mina. The results show that there is a certain correlation between LST, NDVI, and NDBI and elevation in the study area. Using Earth observation technologies can help in studying climate change.

Managing scale and complexity in recurring religious mass gatherings: Preparedness for the Nashik Kumbh Mela 2027

In the context of evacuation planning at a strategic level, an efficient model is proposed to augment network capacity under a budget constraint so as to enhance operational performance under a security threat. Contraflow strategies and lane additions are considered for capacity augmentation. The corresponding mathematical programming model is obtained through a case-specific graph theoretic transformation of Daganzo's cell transmission model, exploiting problem characteristics to enhance computational efficiency. This paper focuses on experiments and sensitivity analyses to provide insights: (1) on the computational efficiency of the proposed model; and (2) for strategies for evacuation planning and operations. Sensitivity analyses are performed for a test network in terms of the budget for contraflow operations, the evacuee population size, and the amount and spatial distribution of origin-destination (O-D) demand. The results suggest that there is a threshold budget beyond which benefits, in terms of the network clearance time, are negligible. They also indicate that the network clearance time varies linearly with uniformly distributed population size. Finally, greater spatial uniformity of O-D demand reduces clearance times, suggesting that specifying multiple destinations in the safety zone may lead to more efficient evacuation in many instances. From a response standpoint, the study suggests that operational effectiveness requires predetermined contraflow strategy plans with designated and well-trained personnel, rather than just reacting to a security event in real-time.

Robust scheduling for large scale evacuation planning

The computer simulation technology can reproduce the crowd movement trend under the emergency situation in a real all-round way, summarize the movement law, and formulate the emergency plan of public places. It can conduct scientific analysis and guidance on crowd evacuation in emergency situations through comprehensive evaluation of crowd evacuation channels in buildings and public places in combination with the emergency situations that can occur in the movement behavior simulation scenarios in emergency situations. In terms of security research hotspot, the emotion of personnel in emergency has a direct impact on crowd evacuation, so it is necessary to summarize Arnold’s emotion model. The occurrence of adverse events in emergency should be avoided to achieve more efficient evacuation of personnel.

Studies on crowd characteristics and management strategies are imperative for modern metro stations. Previous studies mainly focused on pedestrian facilities like sidewalks or crossing, while this research aims to evaluate the efficiency and safety performance of metro stations by introducing a new concept, pedestrian level of service (PLOS). The authors divide a station into separate locations in four typical types: exits, stairways, corridors, and open areas. The characteristics of pedestrians in these four types of locations are delineated and a new index called the interaction index (II) is proposed to quantify the performance. By integrating the II values of all single locations, the PLOS of the whole station can be analyzed. To validate this method, field data were collected and compared. Finally, the Caobao Road metro station in Shanghai was used as a case study for the practical application.

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios.

Mass gatherings (MGs) occur for many different reasons including religious, social, cultural, political and sporting events. There are two types of spontaneous and organized MGs. The first one takes place when a population displaces due to natural disasters or conflicts, whereas the second one is seen in a preplanned program. Mass gatherings are associated with health risks of infectious disease outbreaks, noncommunicable diseases, injuries, terrorist attacks, stampedes, etc. It has been estimated that 70% of human stampedes occur due to religious assemblages. Sports, entertainments, political demonstrations and celebrations are other causes. There are two types of stampede. The first type is a panic behavior of people who afraid of a detected threat, while the second type takes place when the rush is toward something seen as gratifying, such as when spectators poured into a stadium that was already full to over capacity. Stampedes mainly occur in developing countries and its mortality is eight-fold higher in developing compared to developed countries. A terrible stampede took place a few months ago in Mina, Saudi Arabia during Hajj 2015 and caused more than two thousand deaths among pilgrims. The victims came from about 30 different countries and the highest frequency rates were from Iranian nationality. Stampede is a mass casualty incident repeating every few years during Hajj and many pilgrims have lost their lives in this ritual during the past 25 years. Some of the death rates are as follows: 1,426 in 1990, 270 in 1994, 118 in 1998, 35 in 2001, 14 in 2003, 251 in 2004, 3 in 2005 and 360 in 2006. Therefore, this event is the deadliest stampede happened in the recent decades in Hajj. Detailed data are required to analyze stampedes accurately; however, the sources of most information regarding such events are news which is not scientifically valid and scientific papers have also received little attention in definition of preventive strategies. It seems that Hajj authorities should implement new reforms based on Haddon matrix, which categorizes injuries to three phases of precrash, crash and postcrash and classifies underlying causes to four factors of host, agent, physical and social environments. More investigations along with original researches are needed to evaluate Hajj stampedes. In addition, new reforms for prevention of injuries and deaths are mandatory... Language: en

Forecasting occupancy status is crucial for various real-time applications such as crowd management, evacuation, energy management, and others. Using machine learning can provide occupancy information and forecasting with high accuracy. Having abundant and convenient access to sensors and environmental data has allowed research in occupancy prediction to be done in less invasive ways. Deep learning models such as transformers can be used to predict future time steps of occupancy values using data from previous time steps. Environmental sensors such as CO2 and temperature, commonly found in smart homes, are a great way to be able to gather the data needed for occupancy prediction models. They are also much less invasive and intrusive as opposed to cameras and microphones. In this work, we present a transformer-based, machine-learning model, for future time-step prediction using the previous occupancy count. This work expands on our previous work where we estimated the current occupancy count based on the sensor data at that time step. Using the estimated occupancy count till time t, we were able to estimate the occupancy at t+1 with a high accuracy of about 97%.