Are CCTV's Enough to Keep the Public Safe?

Public transport is the lifeline of every city. Millions depend on buses, metros, and trains every day. But with such massive movement, safety becomes a real challenge. From overcrowding to thefts, managing security in transit is not easy. This is where video analytics for public transport is stepping in.

AI video analytics shifts surveillance from passive monitoring to intelligent, real-time safety. It spots risks instantly, minimizes human error, and quickens response, delivering critical improvements for industries where seconds matter.

The Rising Demand for AI Video Analytics

Ridership is rebounding and public tolerance for disruption is low. Public transit in the U.S. is making a strong comeback. An April 2025, Verified Market Research report shows that ridership is back to 85% of pre-pandemic levels, with buses and demand-response services leading at 93%.

Meanwhile, India’s metro expansion has shown remarkable scale. In 2025, India’s metro and rapid rail systems achieved a daily ridership of 10.2 million, according to the Economic Survey report. India also surpassed 1,010 km of operational metro lines across 23 cities, a clear signal of explosive growth in urban mobility.

Predictive Analytics Avoid Accidents Before They Occur

AI-powered video analytics is not just about real-time detection, it’s about foresight. By studying patterns over time, the system can anticipate risks before they escalate. For instance, if a traffic signal consistently malfunctions during peak hours or a bus frequently swerves outside its lane, AI detects the recurring pattern and alerts operators before it results in an accident.

These predictive insights enable transport authorities to prevent incidents, optimize traffic flow, and minimize costly downtime. On highways, AI can track vehicle congestion and unusual driving behavior to forecast potential pileups, allowing quick intervention to avert large-scale accidents.

According to MarketsandMarkets, the global video analytics market was valued at $6.9 billion in 2022 and is projected to reach $22.6 billion by 2028. This rapid growth reflects how transportation and other critical sectors are increasingly using AI to set new benchmarks for safety and efficiency.

From Watching to Understanding: AI Transforms Public Transport Video

AI video analytics turns camera feeds into actionable intelligence. In public transport, that shift delivers real-time insights across safety, efficiency, and passenger experience:

Platform & Track Safety: Detects people or objects in danger zones like track beds or platform edges and triggers alerts or train speed restrictions. In depots, it keeps pedestrians out of bus reversing lanes.
Crowd & Queue Management: Maps density and flow to predict congestion at gates, escalators, and corridors. Real-time alerts help staff redirect passengers before choke points form.
Reducing Dwell Time: Measures boarding, alighting, and door blockages to cut hidden delays. These insights guide stop design and operational tweaks.
Incident Detection: Spots slips, falls, or medical events. Geotagged alerts let responders act quickly and reach the exact location.
Revenue Protection: Identifies fare evasion patterns like tailgating or rear-door entry while safeguarding privacy with event-based monitoring.
Accessibility & Experience: Tracks elevator and escalator queues, assists wheelchair users, and shares real-time crowding data so passengers can choose less crowded cars.
Perimeter & Depot Security: Prevents trespass, theft, or vandalism by distinguishing loitering from normal flow, and even detecting animal incursions near tracks.

Data-Driven Insights

AI video analytics is not just reactive. It is predictive. Every incident, every alert, and every movement becomes data. Over time, this builds a map of patterns and trends that help operators make smarter decisions.

For instance:

Pinpointing peak travel hours where overcrowding risks are highest.
Recognizing stations or routes with repeated fare evasion patterns.
Detecting long-term bottlenecks in passenger flow at ticket gates or escalators.

These insights help transit authorities to redesign passenger flow, adjust train frequencies, or plan targeted interventions. Instead of scrambling to fix problems after they occur, cities move toward anticipating and preventing them; building safer, more efficient journeys for every commuter.

Seamless Integration with Existing Infrastructure

A common misconception is that bringing AI video analytics into public transport means replacing every camera across stations and vehicles. In reality, most solutions work with existing CCTV networks, instantly upgrading them into smart, AI-powered systems. This makes adoption faster and far more cost-effective.

Cloud-based platforms further simplify deployment, allowing city authorities to scale monitoring across multiple stations, depots, and routes without heavy infrastructure investments. At the same time, edge computing ensures analytics run close to where footage is captured, reducing delays and improving real-time accuracy.

For transport operators, that means modernizing safety and efficiency without overhauling the entire system, turning everyday surveillance into a proactive, intelligent network.

Ethics, Privacy, and Public Trust

Transit runs on public trust. If analytics feels like surveillance of people rather than for people, adoption stalls. Several global pilots have sparked debate around accuracy, emotion inference, and transparency. The lesson: the deployments are narrowly scoped, proportionate, and explainable, and avoid speculative use cases like emotion detection that lack scientific reliability and social license.

A credible approach for agencies and operators:

Privacy by design: process at the edge; store events, not identities; use of blurring/redaction on review.
Purpose limitation: sticking to clearly stated safety/operations objectives (e.g., track-intrusion, crowding).
Independent testing: publish accuracy, bias, and false-alarm metrics; allow third-party audits.
Community engagement: signage, public FAQs, and opt-in mechanisms where feasible.

Integration and Implementation

Successful adoption of AI video analytics in transport hinges on structured execution:

Prioritize high-impact use cases: Start with track intrusion, crowd density at gates, rear-door bus boarding, slip/fall detection, and depot perimeter monitoring.
Define clear KPIs: Establish measurable baselines to track performance and justify scaling.
Account for field conditions: Calibrate systems for lighting, camera angles, and seasonal variations.
Leverage system integration: Automate responses, crowd thresholds triggering PA messages, intrusions activating restrictions and CCTV feeds, anomalies dispatching staff.
Ensure transparency: Communicate monitoring scope, data retention policies, and review accuracy regularly.

The Scanalitix Advantage: Transit-Ready Analytics That Scale

Scanalitix turns multi-modal video into actionable operations and safety intelligence without forcing a rip-and-replace of the existing CCTV. For public transport operators, the platform delivers:

Rail-grade safety analytics: platform edge/track-intrusion, slip/fall detection, object left behind, and tunnel entry alerts, designed for low-latency, edge inference.
Crowd & dwell intelligence: density maps at gates and escalators, car-level load estimations, and dwell causality (door blockage vs. boarding surge) to target interventions.
Revenue protection: fare-evasion patterning (tailgating, gate jumping, rear-door boarding) summarized as privacy-preserving events, ready for dashboards and rosters, no PII needed.
Integrated response: Video Management System (VMS), Comprehensive Monitoring System (CMS) and Field-operations (FSM) applications so alerts become actions, from automated alerts to responder dispatch.
Cloud, edge, or hybrid: deploy on existing gateways, or in the cloud, with model updates orchestrated centrally and health monitoring for every camera.

The result: faster, safer journeys; fewer revenue leaks; and staff who can focus on decisions, not screens.

Safer, Smoother Network

Public transport doesn’t need more video; it needs more value from the video it already has. AI video analytics if done right, delivers that value in milliseconds, not months. As ridership climbs and expectations keep rising, agencies that operationalize computer vision will cut incidents, compress delays, and rebuild trust with riders who simply want to get where they’re going, safely and on time.

In scaling, Scanalitix helps prioritize use cases, instrument KPIs, and integrate with the running systems, so every camera becomes a contributor to a smarter, more reliable network.