Docker Container based Crowd Control Analysis Using Dask Hadoop Framework

Abstract

Crowd control is a public policy technique in which massive crowds are handled in order to avoid the emergence of possible issues or threats caused by COVID-19 and over-crowding. In this pandemic, social distancing is critical as there is a high chance of being infected in a crowd. With mounting fears about public disease transmission, the significance of crowd monitoring is crucial in these testing times. In the existing system, the model takes more time and resources to process the data from the crowd control application thus resulting in delayed prediction. Early prediction of the crowd level will help people and other government agencies to control and monitor the crowd. Hence, the main goal of the proposed system is to process a large amount of input from the crowd control application in minimal time using Dynamic Task Scheduling (Dask) based Hadoop framework in a multi-node docker cluster. The multi-node cluster processes the input data in different clusters. Each cluster data is fed to model for prediction and forecasting the count of crowd at a location. The models considered for evaluation are RNN_LSTM and ARIMA. The results shown that RNN_LSTM model has provided better accuracy of 97% compared to the ARIMA of 89%. The results show that the prediction performance of RNN_LSTM has shown 40% decrease in Mean Absolute Error (MAE) and 30% decrease in Root Mean Squared Error (RMSE) over the existing ARIMA model. The proposed system is available as an application to the public and enable them to decide whether to visit a particular place or not.