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Limitmmin anylogic4/11/2023 Role 1 has the paramount importance in casualties’ reduction in the current operations representing a complex system. Role 1 medical treatment at the battalion level command focuses on the provision of the primary health care being the very first physicians and higher medical equipment intervention to casualties’ treatment. 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. 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. The developed datasets present two types of crowd anomalies namely, high densities and contra-flow walking direction. 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 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. Consequently, crowd management literature has adopted simulation tools for generating synthetic datasets to overcome the challenges associated with their real counterparts. 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. Using real crowd datasets can produce effective and reliable 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). Crowd monitoring produces vast amounts of data, with features such as densities and speeds, which are essential for training and evaluating crowd learning models. Developing an SCM solution involves monitoring crowds and modelling their dynamics. Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by implementing efficient crowd learning models that can anticipate critical crowd conditions and potential catastrophes. It is concluded that the dual behavior of e-scooter users (pedestrian or vehicle) poses new challenges that can be met through the development of new extensions or hybrid simulation models. We end up with a dilemma or a scalability problem: to model e-scooter riding behavior in link level or e-scooter services in network level. Our results reveal the advantages and disadvantages of each model in simulating flexible transport modes and services. The ten criteria refer to the capabilities of each model to (a) adjust in new challenges via an open-source code, (b) model shared mobility modes, (c) perform large scale simulation, (d) describe spatiotemporal variation of demand, (e) simulate bicycle, (f) pedestrian, and (g) mixed traffic (h) consider socio-demographic characteristics, (i) integrate new choice models, and (j) model multimodal trips. To test suitability of each model for simulating e-scooter sharing services, we developed an evaluation checklist based on empirical findings. Initially, existing ABMs are described based on ten descriptors. This study aims to bridge the gap between ABMs and e-scooter sharing services by reviewing the existing ABMs and conducting a qualitative assessment. Compared to other shared mobility modes (e.g., autonomous buses and electric taxis), for which Agent-based Models (ABMs) have been applied in many cases, just a few studies attempted to simulate e-scooter trips. E-scooter sharing services have grown exponentially in many cities of the world within the last 10 years, mainly with the goal to serve first/last mile trips.
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