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To provide high quality services under conditions of crowding, it is important to understand the relationships between crowding and personal control. Indeed, in recent years, there has been growing belief that personal control is significant in coping with crowding. However, most studies have been of limited theoretical and practical value because they did not provide an integrated conceptualization of crowding. The results of a field study demonstrate that the personal control‐crowding relationships depend on the individual's crowding experience and the nature of personal control.

The purpose of this paper is to examine how crowding without violating personal space influences consumers’ channel selection and the underlying mechanism of this process. Crowded environment is ubiquitous and affects consumers’ behaviors. However, less attention has been paid to whether and how crowding influences consumers’ preference for purchasing channels.

There were three studies to test the validity of the theorized model, including two laboratory experiments and a field study. The variance analyses and mediation analyses were used to give more insights into the analytical process.

This study proposes that crowding makes consumers lose their perceived control, leading them to form certain compensatory behavior through the conversion between online and offline purchasing channels – the type of goods moderates the process of compensatory behavior.

The results of this study are helpful for retailers to design effective strategies to allocate resources into online or offline channels and to choose the appropriate types of product to promote.

Environmental clues have been widely studied in previous marketing research. Crowding, as a common environmental clue, has only been noticed in recent years. This study examines the impact of crowding on consumers’ channel preference. The results of three studies have confirmed that consumers have higher preference for offline shopping when they are in a crowded environment and found the intrinsic mechanism and the marginal scenario of this process.

The era of crowd network is coming and the research of its steady-state is of great importance. This paper aims to establish a crowd network simulation platform and maintaining the relative stability of multi-source dissemination systems.

With this simulation platform, this paper studies the characteristics of “emergence,” monitors the state of the system and according to the fixed point judges the system of steady-state conditions, then uses three control conditions and control methods to control the system status to acquire general rules for maintain the stability of multi-source information dissemination systems.

This paper establishes a novel steady-state maintenance simulation framework. It will be useful for achieving controllability to the evolution of information dissemination and simulating the effectiveness of control conditions for multi-source information dissemination systems.

This paper will help researchers to solve problems of public opinion control in multi-source information dissemination in crowd network.

This paper uses recent developments in crowd psychology as the basis for developing new guidelines for public order policing. Argues that the classical view of all crowd members as being inherently irrational and suggestible, and therefore potentially violent, is both wrong and potentially dangerous. It can lead to policing strategies that respond to the violence of some in the crowd by clamping down on all members, and therefore lead all members to perceive the police as hostile and illegitimate. In such conditions, even those who were initially opposed to violence may come to side with more conflictual crowd members and hence contribute to an escalation in the level and scope of collective conflict. This paper argues that police officers need to concentrate on understanding the collective identities, priorities and intentions of different groups in the crowd and give the same priority to facilitating the lawful intentions of some groups as to controlling the unlawful intentions of others.

The purpose of this paper is to examine crowd violence in relation to the 2011 Stanley Cup Riot from the perspective of those police officers who were involved in the event, and identify any lessons that can be learned from this incident.

A total of 460 Vancouver police officers participated in this study. Police perceptions of the riot were elicited via mailed questionnaires, which focused on key themes related to crowd disorder and police response, including preparations, deployment, training, safety, causes, and future prevention. Questionnaire responses were analyzed using descriptive approaches and one-sample t-tests.

The study findings revealed that, in general, police felt ill-prepared prior to the riot, and ill- and unsafe during the riot. Their responses also suggested that their negative perceptions of the riot were the result of a few prominent factors, namely inadequate deployment, a lack of operational planning, and the presence of a large and belligerent crowd.

This study represents an important first step in understanding the issues associated with preventing and containing disorderly crowds from the perspective of police officers. Although valuable, this study does have a few limitations, including post hoc data collection.

This study provides important insights into the causes of riots and the particular difficulties officers face during the course of a riot. These insights may be used by departments to assist with planning and preparations related to preventing riots and effectively limiting crowd disorder.

This is the first study to examine police perceptions in a Canadian context. It utilizes a comparatively large sample, and is among the very few studies that includes the views of officers who have actually experienced a riot.

Entertainment is now the largest trend in retailing and urban redevelopment, and is credited with revitalizing many downtowns. Consequently, many other cities are attempting to replicate this success by developing pedestrian‐oriented entertainment districts consisting of movie theaters, nightclubs, bars, restaurants, and retail shopping. This paper summarizes the results of 30 police‐manager interviews and a nationwide survey used to identify entertainment‐district features that create problems and demands for police resources, and effective strategies and tactics for policing entertainment districts. The most effective policing is done at the district planning and design stage by engineering out features that cause problems. Good entertainment‐district policing then requires continuous training and education of business managers and private security personnel. Finally, night‐time district patrol requires a multi‐prong effort to pre‐empt problems and assure public safety.

This paper reviews the human behaviour and risk communications which occurred during a number of major fires (Beverly Hills Supper Club, Summerland, Woolworth’s, Bradford, King’s Cross) and a crowd crush (Hillsborough). The paper draws on official Inquiry reports and related research, including a series of five underground station evacuation studies modelled on the King’s Cross fire scenario. The pattern of delay in warning the public is considered in terms of misconceptions about “panic” and the performance of public facilities as a communication system consisting of design, technology, management and occupancy (setting in use). The paper advocates performance‐based design, warning system technology and facilities management (organisational and occupant response) criteria, allied to minimally sufficient early warning of the public on a risk communication timeline. The latter needs to address and accommodate the timing and duration of occupant response, shelter and escape behaviour from different locations as an emergency unfolds.

This study aims to propose a new simulation approach for a real-life large and complex crowd management which takes into account deep learning algorithm. Moreover, the proposed model also determines the crowd level and also sends an alarm to avoid the crowd from exceeding its limit. Also, the model estimates crowd density in the pictures through which the study evaluates the deep learning algorithm approach to address the problem of crowd congestion. Furthermore, the suggested model comprises of two main components. The first takes the images of the moving crowd and classifies them into five categories such as “heavily crowded, crowded, semi-crowded, light crowded and normal,” whereas the second one comprises of colour warnings (five). The colour of these lights depends upon the results of the process of classification. The paper is structured as follows. Section 2 describes the theoretical background; Section 3 suggests the proposed approach followed by convolutional neural network (CNN) algorithm in Section 4. Sections 5 and 6 explain the data set and parameters as well as modelling network. Experiment, results and simulation evaluation are explained in Sections 7 and 8. Finally, this paper ends with conclusion which is Section 9 of this paper.

This paper addresses the issue of large-scale crowd management by exploiting the techniques and algorithms of simulation and deep learning. It focuses on a real-life case study of Hajj pilgrimage in Saudi Arabia that exhibits intricate pattern of crowd management. Hajj pilgrimage includes performing Umrah along with hajj that involves several steps which is a sacred prayer of Muslims performed at different time span of the year. Muslims from all over the world visit the holy city of Mecca to perform Tawaf that is one of the stages included in the performance of Hajj or Umrah, it is an obligatory step in prayer. Accordingly, all pilgrims require visiting Mataf to perform Tawaf. It is essential to control the crowd performing Tawaf systematically in a constrained place to avoid any mishap. This study proposed a model for crowd management system by using image classification and a system of alarm to manage millions of people during Hajj. This proposed system highly depends on the adequate data set used to train CNN which is a deep learning technique and has recently drawn the attention of the research community as well as the industry in changing applications of image classification and the recognition of speed. The purpose is to train the model with mapped image data, making it available to be used in classifying the crowd into five categories like crowded, heavily crowded, semi-crowded, normal and light-crowded. The results produce adequate signals as they prove to be helpful in terms of monitoring the pilgrims which shows its usefulness.

After the first attempt of adding the first convolutional layer with 32 filters, the accuracy is not good and stands out at about 55%. Therefore, the algorithm is further improved by adding the second layer with 64 filters. This attempt is a success as it gives more improved results with an accuracy of 97%. After using the dropout fraction as a 0.5 to prevent overfitting, the test and training accuracy of 98% is achieved which is acceptable training and testing accuracy.

This study has proposed a model to solve the problem related to estimation of the level of congestion to avoid any accidents from happening because of it. This can be applied to the monitoring schemes that are used during Hajj, especially in crowd management during Tawaf. The model works as such that it activates an alarm when the default crowd limit exceeds. In this way, chances of the crowd reaching a dangerous level are reduced which minimizes the potential accidents that might take place. The model has a traffic light system, the appearance of red light means that the number of pilgrims in a particular area has exceeded its default limit and then it alerts to stop the migration of people to that particular area. The yellow light indicates that the number of pilgrims entering and leaving a particular area has equalized, then the pilgrims are suggested to slower their pace. Finally, the green light shows that the level of the crowd in a particular area is low and that the pilgrims can move freely in that area. The proposed model is simple and user friendly as it uses the most common traffic light system which makes it easier for the pilgrims to understand and follow accordingly.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

Crowded urban regions pose a complex urban challenge that can adversely affect urban residents, encompassing aspects like mental and physical well-being, overall livability and quality of life. The complexity in determining the factors influencing the crowding perception, which encompass subjective and situational psychological factors alongside physical and environmental attributes, imparts ambiguity to planners' approach. This study aims to unravel the intricate interplay between crowding and the physical attributes inherent in the built environment.

This literature review analyses theories linking urban planning and environmental psychology to uncover gaps in the relationship between urban design principles and residents' perceptions of crowding. It also explores influential variables affecting crowding perception and diverse methodologies across contexts.

The study built upon a broad literature review which is expected to summarise and classify the variables of urban planning components and approaches according to their impacts on the psychological perception of crowding. Furthermore, highlighting a number of recommendations that can be considered a guide for planners and urban designers to enhance the urban experience and reduce the perception of crowding.

This study seeks to improve the overall experience of crowding in densely populated urban areas. It accomplishes this by identifying influential factors and comprehending the associated outcomes in such contexts. Furthermore, it bridges perspectives from various fields to examine relevant policies and strategies to mitigate crowding consequences.