No abstract

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.