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This paper thoroughly investigates several approaches to implementing the GNSS network-based real-time positioning technique, which requires the estimation of atmospheric corrections on an epoch-by-epoch basis for RTK. In this study, a network of Continuously Operating Reference Stations in New South Wales, known as CORSnet-NSW, was utilised to: 1) obtain atmospheric residuals from each reference station, and 2) determine network correction for a rover operating in the area covered by the network using several interpolation methods. Applying the atmospheric corrections obtained by the interpolation methods, "synthetic" measurements at a virtual reference station are generated and then used for rover positioning. Field tests with various masterrover baseline lengths ranging from 21 to 62km indicate that a range of 1.9 to 6.5cm of horizontal positioning accuracy is achieved. In this study, the performance of geostatistical (Oridinary Kriging Method and Least Squares Collocation Method) and deterministic (Linear Combination Method, Linear Interpolation Method, Low-order Surface Method and Multiquadric Surface Fitting Method) interpolation methods used in GNSS network-based RTK positioning were also analysed in order to identify the optimal method for mitigating atmospheric effects for real-time kinematic applications under different network geometries.

With the coronavirus (COVID-19) pandemic continuing to spread around the globe, there is an unprecedented need to develop different approaches to containing the pandemic from spreading further. One particular case of importance is mass-gathering events. Mass-gathering events have been shown to exhibit the possibility to be superspreader events; as such, the adoption of effective control strategies by policymakers is essential to curb the spread of the pandemic. This paper deals with modeling the possible spread of COVID-19 in the Hajj, the world’s largest religious gathering. We present an agent-based model (ABM) for two rituals of the Hajj: Tawaf and Ramy al-Jamarat. The model aims to investigate the effect of two control measures: buffers and face masks. We couple these control measures with a third control measure that can be adopted by policymakers, which is limiting the capacity of each ritual. Our findings show the impact of each control measure on the curbing of the spread of COVID-19 under the different crowd dynamics induced by the constraints of each ritual.

Crowd management is a flourishing, active research area and must be given attention due to the potential losses, disasters, and accidents that could occur if it were neglected. For the last decade, the crowd management field has witnessed significant advancements; however, more investigative work is still needed. The integration of different crowd detection and monitoring techniques can enhance the control and the performance compared to those of more limited stand-alone techniques. Crowd management encompasses an entire process, from the monitoring stage through the decision support system stage. This sector involves accessing and interpreting information sources, predicting crowd behavior, and deciding on the use of a range of possible interventions based on context. This paper shows a fresh conclusive review of the concept of the crowd, discussing it from several perspectives in light of its defining characteristics, its risks, and tragedies, which may occur due to challenges faced during crowd management, where these conclusions are based on a massive number of scholarly articles that were newly published. Besides, a systematic discussion is shown concerning the steps of managing a crowd, including crowd detection, in which several new methods are reviewed, followed by illustrating both direct and indirect approaches to crowd monitoring and tracking monitoring. The primary purpose of this review is to establish a comprehensive understanding of crowdrelated processes. Moreover, it aims to find research gaps to overcome the limitations of using stand-alone techniques in each process and provide support to other researchers' future work.

Annually, a huge number of pilgrims visit Mecca to perform Al Hajj ritual. Crowd management is critical in this occasion in order to avoid crowd disasters (e.g., stampede and suffocation). Recent studies stated that various factors, such as the environment, fatigue level, health condition and emotional status have a significant effect on crowded events. This calls for a need for an automated data analytics system that feeds event organizers with information about those factors on real-time, at least from a generalizable sample of crowd subjects, in which proactive crowd management decisions are made to reduce overall risks. This paper develops a novel methodology that fuses mobile GPS and physiological data of Hajj pilgrims collected through wearable sensors to train three classification models: (a) current performed Hajj activity, (b) fatigue level, and (c) emotional level. In a pilot experiment conducted against two subjects, promising results of a minimum of 75% accuracy levels were achieved for the activity recognition and fatigue level classifiers, whereas the emotional level classifier still requires further refinements.

With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population. Two control measures are considered: selecting participants based on vaccine efficacy and restricting the event capacity. We build the model using agent-based modeling to capture the spatiotemporal crowd dynamics and utilize a genetic algorithm to assess the control strategies. This assessment is based on factors that are important for policymakers such as disease prevalence, vaccine diversity, and event capacity. A quantitative evaluation of vaccine diversity using the Simpson’s Diversity Index is also provided. The Hajj ritual is used as a case study. We show that strategies that prioritized lowering the prevalence resulted in low event capacity but facilitated vaccine diversity. Moreover, strategies that prioritized diversity resulted in high infection rates. However, increasing the prioritization of participants with high vaccine efficacy significantly decreased the disease prevalence. Strategies that prioritized ritual capacity did not show clear trends.

There are many problems that procedural algorithms can solve efficiently. However, these algorithms are sometimes too slow to abide by the time available for performing the solution; other times, it is impossible to get a solution using procedural algorithms. A heuristic method is a practical approach that can reach an approximation of an efficient solution where the optimum is not guaranteed. Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. This paper reviews many heuristic-related articles to gather the aspects of the topic in one place and clear the fuzziness to make it easy to comprehend. The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. These techniques are discussed under three classifications: simple heuristics, meta-heuristics, and hyper-heuristics. Most importantly, the paper explores the heuristic role in crowd field problems concluding that heuristics are primarily applied in modeling when it comes to the Crowd field. It investigates different heuristics for crowd management. The main intent of this review is to establish a comprehensive understanding of heuristics-related operations in the crowd management field. Moreover, it aims to support other researchers' future work and fill research gaps by highlighting the absence of crowd problems from heuristics literature and the limitations of each heuristics approach.

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