Activity-travel behavior modeling of pilgrims in mass religious gatherings

Abstract

The number of participants in mass gatherings like Kumbh Mela is ever increasing. Simulations for pre-event crowd modeling, risk assessment, and control planning can help set up robust crowd management and control mechanisms. However, it is necessary to understand better the processes and crowd movement patterns in mass gatherings to model and simulate crowds in such contexts. To this end, the activity-based modeling approach helps analyze the factors that influence different aspects of activity participation and time allocation for pilgrims. These aspects include the type of activities performed, the location and timing preferences for performing these activities, the time spent in different activity locations, etc. A good understanding of these factors can help in better modeling and simulating the spatio-temporal evolution of population density at the location of a mass gathering.

This thesis analyzes the factors influencing activity participation and duration for pilgrim groups and presents corresponding behavioral interpretations and policy implications. The groups mainly comprise non-resident individuals who are not necessarily from the same household. We use the activity diary and demographic data collected in the Kumbh Mela held in Ujjain in 2016 for our analyses. We contribute in the following ways towards the literature through our approach to analyze group behavior in mass religious gatherings: first, we abstract the activities into religious activities at ghat, temple, and camp locations, and identify the group demographic attributes that can influence the activity participation and duration. We then develop empirical models to analyze group activity participation and duration using binary logit, linear regression, and multiple discrete-continuous extreme value (MDCEV) models. The behavioral interpretations, backed up by findings from prior studies, and policy implications, appear consistent across our models. Our MDCEV models may be useful for aggregate activity time allocation along with models for location-specific allocations.