SIMCD: SIMulated crowd data for anomaly detection and prediction

Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by implementing efficient crowd learning models that can anticipate critical crowd conditions and potential catastrophes. Developing an SCM solution involves monitoring crowds and modelling their dynamics. Crowd monitoring produces vast amounts of data, with features such as densities and speeds, which are essential for training and evaluating crowd learning models. By and large, crowd datasets can be classified as real (e.g., real monitoring of crowds) or synthetic (e.g., simulation of crowds). Using real crowd datasets can produce effective and reliable crowd learning models. However, acquiring real crowd data faces several challenges, including the expensive installation of a sensory infrastructure, the data pre-processing costs and the lack of real datasets that cover particular crowd scenarios. Consequently, crowd management literature has adopted simulation tools for generating synthetic datasets to overcome the challenges associated with their real counterparts. The majority of existing datasets, whether real or synthetic, can be used for crowd counting applications or analysing the activities of individuals rather than collective crowd behaviour. Accordingly, this paper demonstrates the process of generating bespoke synthetic crowd datasets that can be used for crowd anomaly detection and prediction, using the MassMotion crowd simulator. The developed datasets present two types of crowd anomalies; namely, high densities and contra-flow walking direction. These datasets are: SIMulated Crowd Data (SIMCD)-Single Anomaly and SIMCD-Multiple Anomalies for anomaly detection tasks, besides two SIMCD-Prediction datasets for crowd prediction tasks. Furthermore, the paper demonstrates the data preparation (pre-processing) process by aggregating the data and proposing new essential features, such as the level of crowdedness and the crowd severity level, that are useful for developing crowd prediction and anomaly detection models

Crowd Recognition System Based on Optical Flow Along with SVM classifier

The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.

Framework development for improving arrival processing of pilgrims at Hajj and Umrah airport terminals

Millions of Muslims around the world perform the Hajj, a mandatory religious journey to the holy city of Mecca, at least once in their lifetime. Therefore, hundreds of thousands of pilgrims arrive weekly at Jeddah and Medina Airports during the Hajj period determined by the Islamic calendar. Numerous research studies have been published on the health, security, risk management and logistics aspects of the mass gathering. However, studies on pilgrims’ wait times, flow and satisfaction at the Hajj and Umrah Terminals (HT)s are very limited. The research evaluating the inbound passenger domain is especially limited. Therefore, this study contributes to the literature by combining different perspectives regarding the inefficiency of HT processes. Furthermore, this study proposes and investigates various aspects to improve the processing of arriving passengers at HTs. It does so by identifying and studying the factors that impede the flow of passengers within these terminals from users’ and providers’ perspectives. This research aims to contribute by developing an innovative integrated framework to improve the flow of pilgrims through arrival terminals and determining how large crowds at airports can be better managed. To meet the study’s aims, a simulation model is developed to verify and confirm the performance of arrival passenger processes at HTs by conducting a mixedmethods analysis and integrating the numerical results of the agent-based and discrete-event simulation models. This study creates a problematic review matrix based on users’ and providers’ perspectives. In addition, the survey on providers’ perspectives indicates that there are five factors, human, infrastructure, operational, technical and organisational factors, influencing arrival passenger processes at HTs and interacting with level of service (LoS) variables. The study indicates the suboptimal processes at airport terminals to focus on the factors negatively affecting the HT processes. In addition, the research highlights the role of terminal configurations. This study compares two airports in terms of peak demand patterns. According to the study, sharp peaks can have strong negative impacts on HTs, while evenly distributed demand can improve LoS at HTs. The simulation model outcomes verify and confirm the parameters and factors influencing LoS. In addition, the study’s integrated framework provides diverse viewpoints on the operational processes at HTs, while the density map matrix helps to classify the processes. This study applies what-if scenarios to identify the impact of pilgrims’ experience and biometric characteristics and finds that inexperience and certain biometric characteristics have negative impacts on LoS. Limitations of the study and suggestions for future research are discussed.Transport System

Leadership behaviour, job satisfaction and the professional identity of medical laboratory staff in Saudi Arabia: An exploratory study

The medical laboratory profession is a highly demanding discipline featuring complex and multi-faceted areas. Its role is to assist healthcare professionals with the diagnosis and treatment of patients and to help control the spread of disease. This study aims to explore the professional identities and job satisfaction of medical laboratory staff (MLS) in two settings in Saudi Arabia (SA) together with the leadership behaviour of their medical laboratory leaders. There is a dearth of studies which have been conducted on MLS in SA, whilst professional identity has yet to be examined. A mixed method study, employing a sequential design was implemented to answer the study’s questions, this employed a range of data collection approaches and took place in two hospitals in SA. The design enabled the researcher to collect various data through questionnaires, in-depth interviews, focus groups, and non-participant observations. Three areas were explored and described: the status of the professional identity and job satisfaction of MLS and the leadership behaviour of medical personnel laboratory directors. In phase one, 99 MLS (response rate 66%) responded to the questionnaires exploring professional identity and job satisfaction, as well as the leadership behaviour of medical laboratory directors. The first phase of the quantitative data was analysed using SPSS software, with descriptive analysis also used. In phase two, in-depth interviews were conducted with purposive sampling of MLS (n=7), supervisors (n=8) and leaders (n=2). Two focus groups were conducted in both settings, and these involved 10 MLS in total. Observational data were also collected, this totalled 96 hours within the work environments of MLS. Interview data, focus groups and observations then all underwent thematic analysis. Phase one demonstrated that MLS scored themselves as average for professional identity and job satisfaction whereas the leadership behaviour scores for the two laboratories were dissimilar. The information in the qualitative findings generally agreed with, and helped to explain, the outcomes of the quantitative data. The phase two qualitative findings identified three main themes for each area. Professional identity themes included the belief in the job’s importance, the need for professional recognition alongside a current lack of role clarity and feeling valued. Job satisfaction themes included job factors, disadvantages of an MLS career and supervisory style and workplace environment. Leadership behaviour themes included effective leadership, as well as the impact of Ineffective leadership and leadership challenges on medical laboratories services such as culture, communication and quality. Generally, in SA the medical laboratory specialty would benefit from further promotion to improve its identity, and the MLS require additional development and training to satisfy their needs. Leadership qualities and skills presented were various and required the development of a culturally competent development programme. As a consequence of these findings, a framework based on the Healthcare Science in NHS Wales programme has been proposed. This was modified to suit the context of SA and would run alongside the Vision 2030 campaign in SA. This is built on three areas of priority; workforce, culture and service