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.