Technology for Crowd Management

Technology for Crowd Management is being widely adopted in India due to the frequency of stampedes across the nation. Read here to know how technology is being used to manage crowds with the example of the Indian Railways using real-time data to manage festive rush.

Ahead of the Chhath festival, the Indian Railways has launched a data-driven, technology-enabled crowd management operation across major stations to handle one of the largest annual human movements in the world.

The initiative combines real-time heat mapping, AI-based demand forecasting, and rapid response mechanisms to ensure passenger safety and smooth travel for over 2.5 crore commuters, most of whom are travelling to Bihar and eastern India.

Table of Contents

Why Technology-Based Crowd Management Is Needed

Mass Gatherings: India hosts some of the world’s largest crowds- the Kumbh Mela, Durga Puja, Ganesh Chaturthi, and political rallies often attract millions.
Urban Density: Metro cities like Mumbai and Delhi face daily challenges in managing crowds at railway stations, markets, and pilgrimage routes.
Disaster Prevention: Stampedes, fires, and structural collapses have prompted a push for proactive crowd risk assessment.
Smart Governance: Aligns with Digital India and Smart Cities missions to enhance public safety through intelligent infrastructure.

Technology for crowd management in India

Technology	Application	Example of Use in India
AI-Based Video Analytics & CCTV Surveillance	Detects overcrowding, unusual movement patterns, and suspicious behavior using facial and motion recognition algorithms.	Used by Delhi Police during Republic Day and at railway stations for real-time crowd monitoring.
Heat Mapping & Geospatial Analytics	Tracks crowd density and movement in real-time using data from cameras, sensors, and mobile signals.	Indian Railways’ real-time heat maps at 35 major stations for managing Chhath festival rush.
Drones & Aerial Surveillance	Provides top-down monitoring of large gatherings and helps authorities identify congestion or panic zones.	Deployed during Kumbh Mela (Prayagraj), Rath Yatra (Puri), and Amarnath Yatra.
Facial Recognition Systems (FRS)	Identifies missing persons, monitors VIP zones, and tracks potential threats in crowded areas.	Implemented at Kumbh Mela 2019 and various airports and metro stations.
Mobile Network Data Analysis	Uses telecom signal density to estimate crowd size and detect sudden surges in real time.	Pilot tested in Tamil Nadu and Maharashtra during major festivals.
Integrated Command and Control Centres (ICCC)	Centralised monitoring hubs under Smart City Mission for surveillance, coordination, and emergency response.	ICCCs in cities like Varanasi, Pune, and Bhubaneswar manage traffic and festival crowds.
AI-Enabled Predictive Modelling	Predicts crowd buildup based on historical data, weather, transport schedules, and social media activity.	Used by Railways and police during elections and religious gatherings.
E-ticketing and RFID Tags	Helps control entry/exit points, reduce queue times, and track crowd flow.	Used during Commonwealth Games (Delhi) and at Tirumala Tirupati Devasthanams (TTD).

Indian Railways’ Crowd management during festivals

The Chhath festival marks one of the busiest travel periods in India, particularly for migrant workers returning to their home states.
In 2025, the Railways plans to operate around 13,000 special trains, nearly double the number of the previous year.
Passenger volume this festive season is estimated to match the population of Australia (2.5 crore people).

Technological Tools for Real-Time Monitoring at Stations

Technology/Tool	Purpose/Function	Example of Implementation
Real-Time Heat Mapping System	Tracks passenger density and movement patterns across 35 major stations using live surveillance, crowd sensors, and ticketing data.	At Udhna (Gujarat), a sudden surge of passengers was detected via heat maps, prompting immediate deployment of standby unreserved trains.
AI-Based Demand Forecasting Model	Uses data from the last two festive seasons (including Diwali) to predict passenger surges and allocate trains accordingly.	Helped identify new high-demand destinations in Bihar, expanded from 8 in 2024 to 28 in 2025.
Automated Alert Categorisation	Classifies station status into three levels: normal, crowded, and overcrowded- triggering corresponding response protocols.	Ambala station (Haryana) triggered an “overcrowded” alert, leading to swift crowd control and standby train operations.
Integrated Command Monitoring	Continuous coordination between Divisional, Zonal, and Railway Board levels for micro-monitoring.	Ensures real-time communication for rerouting or adding services.
Holding Area Construction Plans	Permanent crowd-holding zones at 76 high-footfall stations for crowd dispersal and queue management.	Planned at stations like Mumbai, Howrah, Lucknow, Chennai, Patna, Secunderabad, Guwahati, and Ujjain.

Operational Measures for Crowd and Traffic Control

Standby Trains: Unreserved trains are kept on standby near major junctions to be activated immediately during sudden crowd surges.
Freight Traffic Regulation: Freight services on key routes have been minimized, and Dedicated Freight Corridors are temporarily repurposed to handle passenger trains.
Queue Management: Dynamic queueing systems and crowd marshals are deployed to prevent stampedes at high-density stations.
Micro-Level Coordination: Divisional control rooms communicate directly with local police, station masters, and railway protection forces (RPF) for quick crowd dispersal.

Significance

Passenger Safety: Prevents overcrowding and accidents during high-volume travel periods.
Efficiency: Enables optimal utilization of trains, platforms, and tracks based on real-time demand.
Predictive Governance: Demonstrates how data analytics can transform traditional transport systems into responsive, evidence-based public infrastructure.
Model for Future: Sets a benchmark for AI-driven crowd management in large-scale public events like Kumbh Mela, election periods, or natural disaster evacuations.

Implications

The initiative reflects India’s growing integration of smart mobility technologies in public transport.
It aligns with PM GatiShakti and Digital India missions, showcasing real-time decision-making across multiple agencies.
The long-term plan to build permanent holding areas at high-density hubs marks a shift from ad-hoc management to sustainable crowd infrastructure.

Way Forward

Integrated Crowd Management Framework: Establish national-level coordination under NDMA for technology-based crowd monitoring.
Data Privacy & Ethics: Ensure legal safeguards under the Digital Personal Data Protection Act, 2023.
Public Awareness: Educate citizens about safe crowd behaviour and use of digital information systems.
Local-Level Capacity Building: Train police, disaster response teams, and volunteers in using technology for real-time management.
Public–Private Partnerships (PPP): Collaborate with startups and tech firms to design scalable AI and IoT-based safety solutions.

Conclusion

Technology has transformed crowd management in India from a reactive system to a predictive and preventive safety mechanism. By harnessing AI, IoT, geospatial analytics, and drones, India is setting new benchmarks for managing large human gatherings safely and efficiently.

Indian Railways’ technology-driven crowd management during Chhath 2025 underscores a vital transition in public sector governance, from reactive crisis management to predictive, data-backed operational intelligence.

By integrating real-time heat mapping, AI-based demand analysis, and coordinated response systems, India is not only ensuring safe festive travel but also pioneering a scalable model for managing mass human mobility in a rapidly urbanizing nation.

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