New study reveals how AI can prevent deadly stampedes at mass gatherings like Mahakumbh

The Mahakumbh Mela, like other mass religious events, is structured around sacred temporality, where millions of pilgrims perform rituals at precise, astrologically significant moments. However, this urgency creates high-risk crowding conditions, overwhelming infrastructure and safety measures. The 2025 stampede followed a pattern seen in previous disasters - a pre-dawn surge at the Prayagraj Sangam, overcrowded pathways, and a failure in administrative response.

Millions of devotees gather at the Mahakumbh Mela, drawn by faith and tradition, seeking spiritual fulfillment in one of the largest religious congregations in the world. But time and again, this sacred event has also become the site of catastrophic stampedes, where infrastructural weaknesses, governance failures, and mass crowd surges turn devotion into disaster. The 2025 Mahakumbh stampede in Prayagraj, which claimed 48 lives, eerily mirrored the infamous 1954 Kumbh disaster, where over 700 pilgrims perished under nearly identical conditions. Despite technological progress, stampedes remain a predictable certainty, not an accident.

A recent study, "At the Mahakumbh, Faith Met Tragedy: Computational Analysis of Stampede Patterns Using Machine Learning and NLP", authored by Abhinav Pratap from Amity University, Noida, explores how machine learning (ML) and natural language processing (NLP) can identify historical patterns and offer predictive insights into crowd disasters. Submitted in arXiv (2025), the study reveals how systemic vulnerabilities - narrow riverbanks, high crowd densities (≥8 persons/m²), and administrative lapses - continue to drive fatal stampedes. By examining seven decades of historical inquiry reports and using computational models to analyze crowd dynamics, the research exposes how institutional failures remain unchanged, reinforcing the theory of Institutional Amnesia - a cycle where past mistakes are forgotten, repeated, and normalized.

Stampedes as systemic failures: A historical perspective

The Mahakumbh Mela, like other mass religious events, is structured around sacred temporality, where millions of pilgrims perform rituals at precise, astrologically significant moments. However, this urgency creates high-risk crowding conditions, overwhelming infrastructure and safety measures. The 2025 stampede followed a pattern seen in previous disasters - a pre-dawn surge at the Prayagraj Sangam, overcrowded pathways, and a failure in administrative response.

Using temporal trend analysis, the study found that 92% of past stampedes at the Kumbh occurred near riverbanks or other bottlenecked access points. Furthermore, crowd densities exceeding 8 persons per square meter led to panic propagation, where individual control collapses, and movement becomes dictated by collective force. This phenomenon aligns with Emergent Norm Theory, which explains how ritual urgency overrides individual risk perception, leading to self-reinforcing chaos.

The study's NLP analysis of government inquiry reports (1954-2025) uncovered a disturbing pattern: official narratives systematically attributed disasters to “unforeseen surges”, “crowd mismanagement”, or “unexpected panic” - effectively shifting responsibility away from infrastructure weaknesses and administrative failures. Despite improvements in real-time surveillance and AI-driven monitoring, institutional responses remain reactionary rather than preventive, reinforcing a culture of passive crisis management rather than proactive risk mitigation.

How AI and NLP uncover predictable failures

To quantify risk patterns and assess crowd behavior, the study integrated machine learning algorithms and NLP-driven archival analysis. By applying Support Vector Regression (SVR) and Deep Neural Networks (DNN) to historical data, researchers were able to predict high-risk zones based on past disasters. These AI models identified key triggers that repeatedly led to stampede events, including:

• VIP route prioritization, which diverted safety resources from high-density public zones.
• Infrastructure bottlenecks, where narrow access points failed to handle peak crowds.
• Delayed emergency responses, leading to cascading panic effects.
• Spiritual urgency overriding safety protocols, increasing movement velocity by up to 58% during auspicious rituals.

The statistical modeling confirmed that despite technological advancements, governance effectiveness has remained statistically insignificant in reducing fatalities over time. This suggests that while authorities may implement short-term reforms after each disaster, they fail to institutionalize long-term safety improvements, leading to repetitive cycles of avoidable tragedies.

Institutional Amnesia: Why governance fails to learn

The study’s Institutional Amnesia Theory provides a framework for understanding why disasters at the Kumbh Mela keep recurring. Even after mass casualties in 1954, 1986, 2003, 2013, and 2025, governance strategies have not fundamentally changed. The same mistakes are repeated, often due to:

• Bureaucratic inertia, where safety reforms are implemented only temporarily and later abandoned.
• Discontinuous institutional memory, where lessons from past disasters fail to be documented or integrated into future planning.
• Political prioritization of elite mobility over public safety, leading to security arrangements that benefit VIPs but create congestion in general pilgrimage areas.

A striking example of this was seen in 1954 and 2025, where police resources were diverted for VIP security, leaving critical access routes understaffed and unmanaged, exacerbating fatalities. The regression analysis in the study further demonstrated that high crowd density consistently correlates with fatalities, yet administrative effectiveness remains weak, highlighting that policy interventions have not translated into improved ground-level safety measures.

Moving forward: The need for AI-driven preventive strategies

The research makes a strong case for shifting from reactive disaster response to predictive AI-driven risk mitigation. Some key recommendations include:

• Real-time crowd analytics using AI models to identify risk zones before surges become fatal.
• Dynamic infrastructure adjustments, such as expanding exits based on live crowd density data rather than rigid, pre-planned layouts.
• Decentralizing pilgrimage entry points to prevent high-risk congestion zones at traditional access routes.
• Regulating VIP movement to ensure that security arrangements do not compromise public safety.
• Using NLP and AI-generated risk assessments to inform governance policies that prioritize human lives over ritual adherence.

While faith is an integral part of mass gatherings like the Kumbh Mela, the study highlights the urgent need to rethink how spiritual devotion intersects with public safety. Stampedes are not inevitable, nor are they acts of divine will - they are predictable failures that can be mitigated through data-driven governance, AI-powered risk analysis, and proactive infrastructure planning.

Ultimately, the study reframes stampedes as a preventable collision of faith, infrastructure, and policy inertia. If historical patterns continue to be ignored, the next Kumbh Mela may not just be a celebration of faith, but another tragic entry in an unbroken cycle of preventable deaths.