This study aims to propose a new simulation approach for a real-life large and complex crowd management which takes into account deep learning algorithm. Moreover, the proposed model also determines the crowd level and also sends an alarm to avoid the crowd from exceeding its limit. Also, the model estimates crowd density in the pictures through which the study evaluates the deep learning algorithm approach to address the problem of crowd congestion. Furthermore, the suggested model comprises of two main components. The first takes the images of the moving crowd and classifies them into five categories such as “heavily crowded, crowded, semi-crowded, light crowded and normal,” whereas the second one comprises of colour warnings (five). The colour of these lights depends upon the results of the process of classification. The paper is structured as follows. Section 2 describes the theoretical background; Section 3 suggests the proposed approach followed by convolutional neural network (CNN) algorithm in Section 4. Sections 5 and 6 explain the data set and parameters as well as modelling network. Experiment, results and simulation evaluation are explained in Sections 7 and 8. Finally, this paper ends with conclusion which is Section 9 of this paper.

This paper addresses the issue of large-scale crowd management by exploiting the techniques and algorithms of simulation and deep learning. It focuses on a real-life case study of Hajj pilgrimage in Saudi Arabia that exhibits intricate pattern of crowd management. Hajj pilgrimage includes performing Umrah along with hajj that involves several steps which is a sacred prayer of Muslims performed at different time span of the year. Muslims from all over the world visit the holy city of Mecca to perform Tawaf that is one of the stages included in the performance of Hajj or Umrah, it is an obligatory step in prayer. Accordingly, all pilgrims require visiting Mataf to perform Tawaf. It is essential to control the crowd performing Tawaf systematically in a constrained place to avoid any mishap. This study proposed a model for crowd management system by using image classification and a system of alarm to manage millions of people during Hajj. This proposed system highly depends on the adequate data set used to train CNN which is a deep learning technique and has recently drawn the attention of the research community as well as the industry in changing applications of image classification and the recognition of speed. The purpose is to train the model with mapped image data, making it available to be used in classifying the crowd into five categories like crowded, heavily crowded, semi-crowded, normal and light-crowded. The results produce adequate signals as they prove to be helpful in terms of monitoring the pilgrims which shows its usefulness.

After the first attempt of adding the first convolutional layer with 32 filters, the accuracy is not good and stands out at about 55%. Therefore, the algorithm is further improved by adding the second layer with 64 filters. This attempt is a success as it gives more improved results with an accuracy of 97%. After using the dropout fraction as a 0.5 to prevent overfitting, the test and training accuracy of 98% is achieved which is acceptable training and testing accuracy.

This study has proposed a model to solve the problem related to estimation of the level of congestion to avoid any accidents from happening because of it. This can be applied to the monitoring schemes that are used during Hajj, especially in crowd management during Tawaf. The model works as such that it activates an alarm when the …