From Surveillance to Prevention: The Next Generation of Transit Security

Key Takeaways

• Transit agencies have tens of thousands of security cameras, but most footage is reviewed only after incidents happen, not as it comes in.
• AI video analytics can be layered on top of existing cameras to detect weapons, crowd anomalies, and unsafe behavior before situations escalate.
• Response times can be reduced, resource allocation optimized, safety expanded, and more when moving from passive surveillance to proactive detection.
• Implementation requires agencies to consider privacy, system accuracy, integration complexity, and workforce training alongside technology investment.

Introduction

Public transit ridership is recovering to pre-pandemic levels, but security staffing and resources have not kept pace. Transit agencies have a responsibility to deliver safe rides on every bus and rail line, 24/7, and that’s what passengers expect. Modern transit security relies on physical environmental design, active security staffing, modern surveillance technology, and standard operating procedures working in concert. But a paradox exists: while most transit systems have extensive camera coverage, many security teams still rely on operators to review footage after an incident. The future of transit security is not more cameras. It’s smarter cameras that serve both riders and employees with timely alerts.

The Massive Investment in Transit Surveillance

North American transit agencies have spent decades building vast video surveillance networks, from major cities like Chicago, which today operates more than 33,000 cameras across its bus and rail system, to smaller cities like Portland that have over 10,000 security cameras watching their transportation networks.

These systems were originally designed to perform four basic functions:

• Incident investigation: Reviewing footage after a reported event
• Operational monitoring: Tracking bus and train operations
• Regulatory compliance: Meeting reporting requirements
• Evidence collection: Supporting law enforcement and legal proceedings

As urban centers have expanded, so has the need for strong security to support these core functions. In recent years, cities like New York and Dallas announced expansions to their systems, with MTA adding approximately 12,710 cameras to subway cars and DART announcing plans to upgrade and unify thousands of transit security cameras.

CCTV camera networks help with monitoring activity in heavily trafficked settings and ensuring operations go as smoothly as possible. When an incident happens, they can help guide where responders need to go. The visible security presence they have may also even deter vandalism. Increased transit security helps protect passengers and infrastructure from crime and terrorism. But despite this investment, traditional cameras remain primarily reactive tools-recording what happened rather than detecting what is happening.

Why Recording Is Not the Same as Detecting

There is a fundamental difference between capturing video and identifying threats. Recording documents events for future review. Detection involves recognizing a risk as it arises and responding before the situation escalates. Most security operations centers face challenges that make real-time human monitoring nearly impossible:

• Scale: An organization like CTA has 33,000 feeds running simultaneously. No team can realistically watch them all at the same time.
• Staffing: A limited number of personnel sit in front of screens during any given shift, and even dedicated employees experience cognitive fatigue after long hours.
• Speed: Assaults, weapons drawn, or platform intrusions unfold in seconds-often finishing before anyone at a company monitoring station notices.
• Attention limits: Research consistently shows that the human ability to detect anomalies across multiple video feeds degrades rapidly after about 20 minutes of continuous watching.

The result is a false sense of security. Customers and riders assume cameras equal safety, but detection only happens when someone is actively watching footage, or someone reports the problem by phone or intercom.

The Shift Toward Intelligent Video Analytics

AI-based video analytics add a new layer of protection on top of existing camera infrastructure.  Rather than replacing all the hardware, agencies can use algorithms to scan live feeds for real-time signals of threats. 

While it’s used for a range of different things, one of its strongest use cases is in weapons detection. 

The past few years have seen several shootings and gun-related incidents in public transportation settings. Each of them has effects that ripple into the lives of local communities and commuters nationwide:

• Los Angeles Metro Train Shooting: A 38-year-old man was shot and killed while exiting the E-Line train on June 21, 2024.
• Los Angeles Metro Bus Hijacking: On March 20, 2025, a man wielded what was later determined to be an airsoft gun and seized control of a bus’s wheel, crashing into several cars and a hotel in downtown LA.
• Boston Subway Platform Shooting: A man fired 4-5 rounds on a Harvard University subway platform on April 20, 2025, causing the university to go under a shelter-in-place order.
• Atlanta MARTA Train Shooting: A multi-convicted felon shot at a teenage boy aboard a train car on June 8, 2026.

In response to these, departments of transportation have sought out weapons detection systems that can proactively scan for firearm threats without creating disruptions or operational bottlenecks. 

One such system is an AI gun detection system. They watch live video feeds for visual signatures of firearms and can detect them within a second of becoming visible. Detections are verified by a human (often a professional monitor in an SOC), and then an alert is sent to security teams for further action. When integrated with other security technologies, alerts can trigger automated workflows, announcements via mass notification systems or ENS, and communication with emergency responders so the situation can be dealt with quickly.

These systems are already used and trusted in a range of industries today, including at busy stadiums, in schools and at college campuses, at critical infrastructure sites, and in mass transit hubs across the country.

The priority here is clear: AI helps security professionals assess and prioritize events, not replace human judgment. Every alert still requires a person to verify and decide how to respond.

What Real-Time Threat Detection Changes

The workflow shift from reactive to proactive detection transforms how a security team handles incidents:

Stage	Traditional Workflow	AI-Assisted Workflow
Detection	Rider or operator reports incident	System flags anomaly automatically
Notification	Dispatch receives call, reviews details	Alert with live video clip sent to nearest security officer
Response	Officers dispatched after reporting delay	Officers deployed while event is still unfolding
Outcome	Investigation after the fact	Opportunity to prevent escalation of criminal activity

Automated alerts reduce response times, improve situational awareness across the entire system, and help security teams focus on high-risk events rather than routine noise. 

Transit organizations can ask vendors for proof-of-value for AI analytics solutions to show their commitment to finding the best technology.

Challenges Agencies Must Consider

Deploying intelligent analytics is not without obstacles. Stakeholders, leadership, and the public often have concerns when new technologies are brought into the mix:

• Privacy and public trust: When it comes to expanding surveillance capabilities, civil rights organizations have been apprehensive, particularly with facial recognition and tracking. Transparent policies, community engagement, and compliance with regulations are essential, and choosing weapons detections systems that do not involve biometric data collection can help alleviate these concerns.
• Accuracy: Weapon detection systems achieve up to 99.8% accuracy in controlled conditions, but performance can drop in low lighting, overhead camera angles, or compressed video. Continuously tuning the technology and having operator feedback loops keeps everything working the best it can.
• Integration complexity: Many agencies operate patchwork systems-analog cameras from different vendors, varying resolution, and incompatible protocols. Upgrading infrastructure to support analytics will take a lot of planning.
• Operational protocols: When a crisis is happening, the last thing you want to happen is security teams scrambling to figure out who does what. Defining what kinds of alerts should trigger actions, outlining a clear chain of command, and providing ongoing training sessions for transit security officers keeps everyone feeling prepared. 
• Budget constraints: Security, while important, is just one thing on a high-priority budgeting list. Capital costs for HD cameras, edge computing hardware, licensing, and workforce development compete with other department needs and long procurement cycles. Assessing ROI against reduced incidents, liability, and improved safety and security outcomes can help to justify spending.

What to Look for in an AI Gun Detection Provider

While they share some similarities, AI-powered gun detection solutions are not the same. They each offer varying levels of performance and operational maturity, and they have different capabilities when it comes to integration. As transit agencies evaluate solutions, they need to look beyond marketing claims and focus on the factors that determine whether a system will perform effectively in real, complex transit environments.

Real-Time Detection Speed
Transit incidents unfold in seconds. Agencies should evaluate how quickly a solution can identify a visible firearm, verify the detection, and deliver an actionable alert to security personnel. The value of gun detection lies in providing enough time for intervention before an incident escalates.

Performance in Real-World Conditions
Many AI models perform well in controlled demonstrations but struggle in varying operational environments. This is usually due to differences in training data and modeling. Transit agencies should look for systems that can perform with varying lighting conditions, camera angles, crowded platforms, moving vehicles, weather conditions, and different firearm types. Independent testing results, customer references, and proof-of-value deployments can help validate vendor claims.

Compatibility with Existing Infrastructure
Replacing thousands of cameras is rarely practical. The most effective solutions can be deployed on top of existing video management systems (VMS), CCTV networks, and security operations workflows. Agencies will often choose platforms that support a wide range of camera manufacturers, resolutions, and network configurations.

Human-in-the-Loop Verification
Having frequent false alarms can overwhelm dispatchers and reduce trust in any system quickly. Providers that offer trained human analysts and strong verification workflows can help ensure alerts are reviewed before they’re brought to transit security teams. Human verification also supports more informed decision-making during rapidly evolving situations.

Integration with Emergency Response Workflows
Detection alone does not improve safety unless it triggers action. Agencies should evaluate whether the platform can integrate with mass notification systems, dispatch software, computer-aided dispatch (CAD) platforms, radio communications, and local law enforcement workflows. The goal is to shorten the time between detection and response.

Privacy-First Design
Public trust remains essential for any transit security initiative. Agencies should favor solutions that focus on weapon identification rather than facial recognition, biometric tracking, or personal identification. Providers should be transparent about how video data is processed, stored, and protected.

Scalability Across Large Transit Networks
Even if a solution works great at a single station, it might not perform the same way across an entire transit system. Agencies should consider whether or not platforms can support thousands of cameras, multiple facilities, and geographically distributed operations centers without significant changes to performance.

Proven Experience in High-Traffic Environments
Transit environments can be difficult to secure completely. They involve constant movement, dense crowds, and complex operational schedules. Vendors with deployments in transportation hubs, airports, stadiums, schools, critical infrastructure, or other high-volume public venues often have more experience managing these challenges at scale.

Measurable Operational Outcomes
Ultimately, agencies should ask vendors to demonstrate how their technology improves security outcomes. Key metrics may include detection speed, reduction in response times, alert accuracy, operational efficiency gains, and documented incidents where early detection enabled intervention.

Deploying AI gun detection in the real world should be more than just a technology purchase made by transit agencies. These criteria help to ensure that they are investing in solutions that actually strengthen their ability to identify threats, coordinate responses, and protect riders and employees across the entire transportation network.

Conclusion

Cameras record. Analytics detect. People respond. The transit agencies that will protect riders most in the next decade will be the ones that connect all three into a proactive detection network. The technology exists today to turn existing surveillance infrastructure into something much more powerful, but only when paired with trained personnel, clear protocols, and community trust. The next chapter of public transit safety will be written by agencies that invest not just in more cameras but in the intelligence that makes every camera count.

If you’re a transit security officer considering AI gun detection and how it can work for your department, schedule a conversation with the Omnilert team and get a live demo.

Frequently Asked Questions (FAQs)