Prof Ashish Verma (Associate Professor, Department of Civil Engineering)
Kumbh Mela is the largest religious festival in the world, and involves the pilgrimage of some 100 million Hindus to a sacred river. The next festival will take place in Ujjain in 2016. Unfortunately, many of the previous versions of this event have been marred by accidents and deaths. Human crowds are complex adaptive systems, making predicting their behaviour challenging. The Kumbh Mela Experiment combines geoinformatics and remote sensing with computational science. The project will develop sophisticated methods and algorithms to aid planners and event managers in managing such extremely large crowds.
The planned data collection process will involve three disparate data sources, wrist-bands, cell phone (CDRS analysis) and video recordings. Each of these raw data sources will require completely different methods of processing, and the resulting analysis will likely be at different spatial and temporal scales. Some of the data sources – because of the nature of the collection process or the complexity of the analysis – will lend themselves more easily to real-time processing (or at least very low latency). Finally, the idea will be to fuse the data sources with three goals in minds. Firstly, the data feeds can be used to examine patterns in the crowd and look for early warning signals of impending danger. Secondly, this data will provide information on human crowds at unprecedented scale and resolution. This can lead to fundamentally new understanding of the dynamics of human crowds, for example identifying the densities at which the impact of individual behaviour on macro level dynamics start to diminish (learning whether this happens as a form of phase transition). Finally, the data should be used as input to the simulations.
For more information please visit the project’s homepage.
A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings Journal Article
International Journal of Disaster Risk Reduction, 2017.