| dc.description.abstract | Understanding the principles and applications of crowd dynamics in mass gatherings is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from India and other countries suggest that the crowd crushes in mass gatherings, especially in religious events, frequently occur, highlighting the importance of studying crowd behaviour more scientifically. This is required to support appropriate and timely crowd management principles in planning crowd control measures and providing early warning systems at mass gatherings. Hitherto, the researchers have studied the previous incidents of crowd crushes from the viewpoint of high density and the resulting physical forces and poor geometric facilities, but the factors such as psychological triggers and weather are overlooked. Further, although the average number of victims per panic event seems to decrease, their total number increases with the frequency of mass religious gatherings. Unless proper measures are in place, this trend will continue. Therefore, a comprehensive risk assessment is required to assess the potentially risky situations associated with an event that can lead to crowd crushes.
To manage large crowds, an understanding of crowd dynamics is required to reasonably predict the level of risk and implement appropriate crowd management measures. However, there is a lack of empirical studies with real-world data on crowd behaviour and dynamics. Therefore, deriving motivation from the given background, the objectives of this research are: (1) to conduct a detailed empirical data collection in a mass religious gathering in an uncontrolled setup, (2) to understand the fundamental relationships between speed, flow, and density across different sections of case study, (3) to analyse the potentially risky situations observed in the site, and (4) to develop a comprehensive crowd risk model concerning crowd movement in mass religious gatherings and arrive at a Crowd Risk Index (CRI) which can give a range of values on scale defining the possibilities of crowd risks in a given area of mass religious gathering.
The case study considered was Kumbh Mela 2016, held in Ujjain, India, between 22 April and 21 May. It attracted an estimated population of 75 million with an interesting mix of domestic and international pilgrims, spiritual leaders, and holy men, who journeyed to Ujjain from short duration (one day) to long-term stay (throughout the event). The key attractions of Kumbh were (1) taking a dip in the river Kshipra and (2) visiting temples. Data was collected throughout the event, covering the important days on which the crowd was expected to be more. Data in video form was recorded using Go-Pro, head-mount cameras, mobile phones and CCTV cameras. Additionally, data was also collected using GPS trackers and survey forms. Further, quantitative data was collected through visual observations.
The Crowd Risk Index was developed from three pillars of indices: Crowd Dynamic Index (CDI), Crowd Anxiety Index (CAI), and Temperature-Humidity Index (THI). CDI include (i) macroscopic fundamental flow diagrams of a spiritually motivated crowd (ii) characteristics of stop and go waves in one-dimensional interrupted pedestrian flow through narrow channels (iii) understanding social group behaviour in the crowd and the effect of the presence of groups on the crowd movement, and (iv) understanding serpentine group behaviour and its impact on crowd dynamics.
Using the above-mentioned study observations, the CDI was developed for ghat and temple locations as they were the two key attractions of Kumbh Mela. All the variables were used both for ghat and temple model. About 53 expert opinions were gathered separately for the temple and ghat videos. The experts rated the risk levels from the video clippings as low, medium, or high. Low was taken as class 1, medium as class 2, and high as class 3, which was given as an input to the CDI. The dataset was imbalanced, and so the SMOTE-Tomek Link method was used to balance out the dataset. Cross Validation technique using the Random Forest algorithm was used to predict the level of risk for CDI.
CAI included the patience and aggression scores obtained from the study conducted on understanding the crowd’s emotions. A Structural Equation Modelling (SEM) was performed, and hypotheses testing were done to verify the relationship between the first order (cue-dependence (CD), tolerance (TO) and goal-oriented (GO); norm violation (NV), obstruction to movement (DO) and social display of power (SP)) and second-order factors (patience and aggression). All the first-order factors under patience and aggression were found to have a direct and significant impact on the second-order factors, i.e., patience and aggression, respectively. The patience and aggressions scores were obtained from the path loadings. Moreover, the effect of high temperature can have an indirect impact on the CRI through increasing aggression. This was also included in the index. The dataset here was also imbalanced, and so the SMOTE-Tomek Link method was used to balance out the dataset. The same Cross Validation technique using the Random Forest algorithm was used to predict the level of risk for CAI. A value between 0 and 1is class 1 (low), a value between 1 and 2 is class 2 (medium), and a value between 2 and 3 is class 3 (high).
THI from literature was used to gauge the effect of temperature on the crowd risk. Kumbh Mela 2016 was held during peak summer under the scorching heat. The average temperature across the event duration was above 91-degree Fahrenheit, which implies that the event happened under severe stress conditions. This indicates the importance of including temperature effects into the model, especially for events that happen under high-temperature conditions. The comfort zone values were considered as class 1 (low), mild and severe stress conditions are combined as class 2 (medium), and severe stress conditions as class 3 (high).
The CAI, CDI, and THI together form the CRI. The relative importance of these indices was also gathered from the same 53 experts. The weights were then calculated using the AHP process. Then the final CRI prediction equation was formulated. A CRI value between 0 and 1 indicates low risk, a value between 1 and 2 indicates medium risk, and a value between 2 and 3 indicates high risk. This can help in predicting the level of risk in a given area for every one-minute interval.
Therefore, the CRI developed includes factors such as crowd anxiety and temperature, other than the crowd dynamics and behaviours, as it is important to include a comprehensive set of factors for a better prediction. With an overarching understanding of the factors leading to critical crowd conditions, the CRI developed in this work can help reasonably predict the level of risk and implement appropriate crowd management measures. However, the approach used in the study has its own set of limitations. There are other important factors that could endanger crowd safety, including bottleneck movement and crowd turbulence, among others, which are not considered. Studying and incorporating these into the CRI can result in a more accurate model. Adding health-related aspects and studying other psychological aspects supplemented with video data can also improve the model's precision. In addition, a comparison of different machine learning techniques to assess their performance could be a follow-up to this research. Despite these limitations, the study proposes a novel methodology for predicting crowd risk in mass religious gatherings. This is a one-of-a-kind study in crowd disaster and crowd safety that has never been attempted before in the literature. | en_US |
