How Cities Can Turn Existing Traffic Cameras Into Real-time Safety Intelligence

Cities operate thousands of traffic and curb cameras, yet most remain unmonitored. This blog explains how AI safety intelligence transforms existing camera networks into real-time detection systems that identify hazards, theft, and curb conflicts, without new hardware.

Cities today operate thousands of traffic, curb, ALPR, and intersection cameras. Yet more than 96% of this footage is never monitored in real time. Most of it is used retroactively, long after incidents have occurred.

For transportation departments already stretched thin, this means roadway hazards remain undetected, copper wire theft goes unnoticed until lights go dark, and curb conflicts pile up without visibility into root causes.

But a major shift is underway. With modern AI video analytics, cities can transform their existing camera network without new hardware into a real-time intelligence grid. The result is a safer, more equitable, and more resilient urban transportation system that identifies issues the moment they emerge, rather than when someone reports them.

This is the promise of real-time AI safety intelligence: using the infrastructure cities already have to deliver the operational visibility they’ve always needed.

What “AI Safety Intelligence” Really Means

Most cities think of AI video analytics as basic object detection, spotting cars, people, or specific items. But true safety intelligence is far more advanced.

A modern AI Network Video Recorder (NVR) continuously analyzes real-time video streams for patterns, anomalies, and context-specific behaviors. For example, the VIDIZMO AI NVR overlays an AI layer on top of existing traffic, curb, and ALPR cameras to detect:

• unsafe roadway conditions
• suspected copper wire theft patterns
• ADA and bike-lane obstructions
• double parking
• dumping or debris events
• unauthorized access to city infrastructure

All of this operates without replacing cameras, integrating seamlessly with existing VMS systems and IP video devices. It transforms passive footage into an active intelligence feed that directly supports DOT, public safety, and smart-city operations.

Why AI Safety Intelligence Is Becoming Essential

Let's dive into the specific benefits that AI safety intelligence brings:

Faster Hazard Detection

Historically, potholes, debris, and illegal dumping are discovered only when residents report them or when staff spot them manually during routine patrols. AI changes this model entirely.

AI models continuously analyze video, flagging roadway hazards as soon as they appear. Because detections are timestamped and geotagged, they can be routed directly into work order or 311 systems for validation and dispatch.

This shift from reactive reporting to automated detection dramatically improves outcomes in Vision Zero and roadway maintenance.

Better Infrastructure Protection

Copper wire theft and tampering with streetlight infrastructure cost cities millions annually. But most theft happens at night, in remote segments, with no staff watching cameras in real time.

AI models can detect:

• after-hours loitering near poles or pull boxes
• suspicious tool use
• access to restricted infrastructure
• movement patterns associated with theft

VIDIZMO AI NVR can also correlate suspicious behavior with tamper or outage alerts from smart streetlight systems, enabling immediate intervention.

This reduces streetlight downtime, improves nighttime visibility, and protects city assets that traditionally receive inconsistent monitoring.

Always-On Coverage for Equity

Not all neighborhoods generate the same volume of complaints. High-resource areas tend to submit more 311 reports, while underserved areas may experience hazards longer simply due to lower reporting rates.

AI solves this inequity by ensuring:

• every corridor receives the same level of detection
• hazards aren’t missed simply because residents didn’t report them
• curb misuse affecting vulnerable communities (e.g., ADA access) is surfaced consistently

This supports smarter, fairer policy enforcement and infrastructure management across the entire network.

Operations That Scale Automatically

Human monitoring does not scale. AI does. A single system can analyze:

• hundreds of live streams at once
• across multiple asset categories
• with configurable thresholds and alert routes

DOT staff receive only the detections that matter, reducing noise, fatigue, and operational inefficiencies. This allows enforcement, maintenance, and inspection teams to work proactively rather than reactively.

Top Use Cases Cities Are Prioritizing Right Now

The following are the top use cases where cities are focusing:

Copper Wire Theft Detection

AI identifies suspicious patterns around streetlight poles, cabinets, and pull boxes, especially during late hours. Correlated with activity alerts, these detections allow teams to respond fast and prevent outages.

Roadway Condition Detection

Using both fixed and fleet-mounted cameras, AI can detect:

• potholes
• debris
• trash accumulation
• dumping
• lane obstructions

This provides earlier visibility and reduces the need for manual roadway inspections.

Curb & Parking Conflict Detection

Cities struggle with curb misuse, especially in dense corridors. AI detects:

• double parking
• bike-lane blockages
• ADA ramp obstructions
• misuse of loading zones
• overstaying or boundary violations

These detections can route into enforcement or planning workflows to improve curb turnover and right-of-way safety.

How Cities Can Implement Real-Time Safety Intelligence Quickly

The most efficient path is to deploy an AI NVR that connects directly to existing camera networks, requiring no new hardware. Systems like VIDIZMO:

• support ONVIF and RTSP streams
• work over existing traffic, curb, ALPR, and fleet camera feeds
• integrate with 311/CRM, traffic management, and case systems
• run on SaaS, private cloud, or fully on-premises environments

This allows cities to begin with one corridor, or a single high-impact use case such as copper theft, and later scale across the entire network using the same infrastructure.

Why This Approach Works So Well for DOTs

AI safety intelligence gives DOTs a force multiplier, expanding operational reach, improving response times, and strengthening policy alignment without increasing workload or staffing demands. Here is why this approach works:

No Rip-and-replace

Cities avoid expensive hardware upgrades because AI safety intelligence layers directly onto the cameras and VMS infrastructure they already use. Most DOTs rely on a patchwork of traffic, curb, ALPR, and fleet cameras accumulated over the years. Replacing them would require multi-year procurement cycles, construction closures, and millions in capital funding.

An AI NVR eliminates this need entirely by ingesting live feeds from virtually any IP camera, allowing agencies to modernize capabilities without touching physical assets. This dramatically accelerates deployment timelines and preserves previous investments while unlocking far more value from the existing network.

Interoperable by Design

Because DOT operations depend on multiple systems, including VMS platforms, 311 service request queues, traffic management centers (TMC), and digital evidence repositories. Any AI solution must integrate cleanly with existing workflows.

An interoperable AI layer sends detections, alerts, and metadata into the systems staff already use, avoiding duplication and reducing the learning curve. For example, roadway hazard detections can flow into 311/CRM as draft service requests, while theft-related footage can automatically route into a digital evidence management system for case building.

This interconnected design ensures that AI enhances cross-department operations.

Continuous Improvement

One of the strongest advantages of AI-driven camera intelligence is that the models are not static. They can be fine-tuned using city-specific samples, adapting to unique lighting conditions, camera angles, street layouts, and behavioral patterns.

A model trained on generic datasets may struggle with corridors, but once fine-tuned on local samples, accuracy increases significantly. This iterative approach ensures the system becomes more precise over time, reducing false positives and improving detection fidelity with every cycle.

Data Governance Aligned

Transportation agencies operate under strict security, privacy, and audit requirements. An enterprise-grade AI NVR must therefore support encryption in transit and at rest, SSO/MFA authentication, role-based access control, SCIM provisioning, and detailed audit logs.

VIDIZMO’s platform supports CJIS and FIPS-aligned configurations, on-premises or private cloud deployments, and fully isolated environments when required. This ensures that cities can adopt AI safely, with confidence that resident privacy, sensitive infrastructure footage, and operational data remain fully protected under existing governance policies.

A Smarter, Safer, More Responsive City, Using the Cameras You Already Have

Cities don’t need new cameras to gain new intelligence. By applying AI analytics to existing roadway, curb, and ALPR networks, DOTs can shift from reactive incident management to proactive, real-time transportation intelligence.

This strategic shift delivers:

• faster hazard response
• stronger infrastructure protection
• more equitable service delivery
• reduced operational load
• smarter planning and enforcement

The infrastructure is already in place. AI simply unlocks its full potential.