A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

In emergency management for mass gatherings, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, the literature shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This paper presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data to validate our framework is described

Integrating Social Media with Ontologies for Real-Time Crowd Monitoring and Decision Support in Mass Gatherings

Situation awareness plays an essential role in making real-time decisions in mass gatherings. In the last few years, social media data analysis has been proved to be an effective approach to enable and enhance situation awareness. Mass gathering events are dynamic and critical environments where thousands of people attend. During the event, there is a potential for injuries and other health hazards, and thus it is critical for emergency medical services to access real-time and situational awareness information, especially concerning the nature of the crowd. It has been well recognized in the literature that crowd mood and behaviour can have a direct impact on the crowd safety and patient presentation rates. We describe a mobile social media-enabled crowd monitoring architecture that aims to improve emergency management decision-making by analysing the data from social networks in real-time. The proposed architecture incorporates a crowd behaviour classification model, which facilitates real-time situation awareness and provides a better understanding of analysis results. Awareness and perception of crowd mood and behaviour during the event can significantly improve prediction of patient presentation rates; leading to timely and effective medical care provision. The implementation and evaluation of the proposed framework on an Android mobile phone is described

Dynamic investigation of the City of Manchester Stadium

Proceedings of a meeting held 30 January - 2 February 2006, St Louis, Missouri, USA. http://toc.proceedings.com/00102webtoc.pdf Vol. 2The dynamic behaviour of the City of Manchester Stadium is described in this paper. With a remote monitoring system, developed at the University of Sheffield, output-only vibration response data were acquired during a music concert. Data provided by Dr. P. Reynolds were used for the study. Modal parameters estimations were made on data acquired from different crowd activities. The crowd-structure interaction is also studied. A finite element model has been developed and the crowd has been considered in it. Measured vibrations were compared with numerical results. A load model for future designs has been obtaine

Sensing and perception technology to enable real time monitoring of passenger movement behaviours through congested rail stations

© 2015 ATRF, Commonwealth of Australia. All rights reserved. Passenger behaviour can have a range of effects on rail operations from negative to positive. While rail service providers strive to design and operate systems in a manner that promotes positive passenger behaviour, congestion is a confounding factor, which can create responses that may undermine these efforts. The real time monitoring of passenger movement and behaviour through public transport environments including precincts, concourses, platforms and train vestibules would enable operators to more effectively manage congestion at a whole-of-station level. While existing crowd monitoring technologies allow operators to monitor crowd densities at critical locations and react to overcrowding incidents, they do not necessarily provide an understanding of the cause of such issues. Congestion is a complex phenomenon involving the movements of many people though a set of spaces and monitoring these spaces requires tracking large numbers of individuals. To do this, traditional surveillance technologies might be used but at the expense of introducing privacy concerns. Scalability is also a problem, as complete sensor coverage of entire rail station precinct, concourse and platform areas potentially requires a high number of sensors, increasing costs. In light of this, there is a need for sensing technology that collects data from a set of ‘sparse sensors’, each with a limited field of view, but which is capable of forming a network that can track the movement and behaviour of high numbers of associated individuals in a privacy sensitive manner. This paper presents work towards the core crowd sensing and perception technology needed to enable such a capability. Building on previous research using three-dimensional (3D) depth camera data for person detection, a privacy friendly approach to tracking and recognising individuals is discussed. The use of a head-to-shoulder signature is proposed to enable association between sensors. Our efforts to improve the reliability of this measure for this task are outlined and validated using data captured at Brisbane Central rail station

Are You in the Line? RSSI-based Queue Detection in Crowds

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.Comment: This work has been partially funded by the European Union's Horizon 2020 research and innovation programme within the project "Worldwide Interoperability for SEmantics IoT" under grant agreement Number 72315

Measuring attendance: issues and implications for estimating the impact of free-to-view sports events

A feature of many non-elite sports events, especially those conducted in public places is that they are free-to-view. The article focuses on the methodological issue of estimating spectator attendance at free-to-view events and the consequences of this for impact evaluation. Using empirical data from three case studies, the article outlines various approaches to measuring attendance and discusses the key issues and implications for evaluating free-to-view sports events in the future

The user experience of crowds

This thesis is concerned with the user experience of crowds, incorporating issues of comfort, satisfaction, safety and performance within a given crowd situation. Factors that influence the organisation and monitoring of crowd events will be considered. A comprehensive review of the literature revealed that crowd safety, pedestrian flow modeling, public order policing and hooliganism prevention, has received the greatest attention with previous research on crowds. Whereas crowd performance, comfort and satisfaction has received less attention, particularly within spectator events (sporting and music for example). Original research undertaken for this doctoral thesis involved a series of studies: user focus groups, stakeholder interviews, and observational research within event security and organisation. Following on from these investigations, the findings have been integrated with a tool to assist crowd organisers and deliverers during the planning of crowd events, and accompanying user feedback interviews following use of the tool. The overarching aim of the research within this thesis was to explore the complex issues that contribute to the user experience of being in a crowd, and how this might be improved. The crowd user focus groups revealed differences in factors affecting crowd satisfaction, varying according to age and user expectations. Greater differences existed between crowd users, than across crowd situations, highlighting the importance of identifying expected crowd members when planning individual events. Additionally, venue design, organisation, safety and security concerns were found to highly affect crowd satisfaction, irrespective of group differences or crowd situations, showing the importance of these issues when considering crowd satisfaction for all crowd events, for any crowd members. Stakeholder interviews examining crowds from another perspective suggested that overall safety was a high priority due to legal obligations, in order to protect venue reputation. Whereas, comfort and satisfaction received less attention within the organisation of crowd events due to budget considerations, and a lack of concern as to the importance of such issues. Moreover, communication and management systems were sometimes inadequate to ensure compliance with internal procedures. In addition a lack of usable guidance was seen to be available to those responsible for organising crowd situations. Eleven themes were summarised from the data, placed in order of frequency of references to the issues: health and safety, public order, communication, physical environment, public relations, crowd movement, event capacity, facilities, satisfaction, comfort, and crowd characteristics. Results were in line with the weighting of the issues within the literature, with health and safety receiving the most attention, and comfort and satisfaction less attention. These results were used to form the basis of observational checklists for event observations across various crowd situations. Event observations took two forms: observing the role of public and private security, and observing crowd events from the user perspective. Observations within public and private security identified seven general themes: communication, anticipating crowd reaction, information, storage, training, role confusion, financial considerations and professionalism. Findings questioned the clarity of the differing roles of public and private security, and understanding of these differences. Also the increasing use of private over public security within crowd event security, and the differing levels of training and experience within public and private security were identified. Event observations identified fifteen common themes drawn from the data analysis: communication, public order, comfort, facilities, queuing systems, transportation, crowd movement, design, satisfaction, health and safety, public relations, event capacity, time constraints, encumbrances, and cultural differences. Key issues included the layout of the event venue together with the movement and monitoring of crowd users, as well as the availability of facilities in order to reduce competition between crowd users, together with possible links to maintaining public order and reducing anti-social behaviour during crowd events. Findings from the focus groups, interviews, and observations were then combined (to enhance the robustness of the findings), and developed into the Crowd Satisfaction Assessment Tool (CSAT) prototype, a practical tool for event organisers to use during the planning of crowd events. In order to assess proof of concept of the CSAT, potential users (event organisers) were recruited to use the CSAT during the planning of an event they were involved in organising. Semi-structured feedback interviews were then undertaken, to gain insight into the content, usefulness, and usability of the CSAT. Separately human factors researchers were recruited to review the CSAT, providing feedback on the layout and usability of the tool. Feedback interviews suggested the CSAT was a useful concept, aiding communication, and providing organisers with a systematic and methodical structure for planning ahead, prioritising ideas, and highlighting areas of concern. The CSAT was described as being clear and easy to follow, with clear aims, and clear instructions for completion, and was felt to aid communication between the various stakeholders involved in the organisation and management of an event, allowing information to be recorded, stored and shared between stakeholders, with the aim of preventing the loss of crucial information. The thesis concludes with a summary model of the factors that influence crowd satisfaction within crowd events of various descriptions. Key elements of this are the anticipation, facilities, and planning considered before an event, influences and monitoring during an event and reflection after an event. The relevance and impact of this research is to assist the planning of crowd events, with the overall aim of improving participant satisfaction during crowd events. From a business perspective the issue is important with competition between events, the desire to encourage return to events, and to increase profit for organisers. From an ergonomics perspective, there is the imperative of improving the performance of crowd organisers and the experience of crowd users