Colorado has moved from visible, point-based speed cameras to a system that tracks vehicles across multiple cameras, calculates average speeds over distance, and issues automated fines to registered owners. What started as targeted warnings has become continuous monitoring on stretches like Interstate 25, and the shift raises questions about due process, discretion, and how quickly traffic tools can turn into persistent surveillance.
The state’s Automated Vehicle Identification Systems now measure average speed over a corridor instead of a single spot, flagging vehicles that exceed the limit by 10 miles per hour or more. Those detections trigger a $75 citation that carries no license points and is sent to the vehicle’s registered owner. The change moves enforcement from a momentary observation to a persistent calculation based on location data.
“Meanwhile, the state continues issuing tickets at scale, backed by a system that never sleeps, never questions itself, and never exercises judgment.”
Officials pitch this as a safety measure, especially through work zones where consistent speeds matter. But the mechanics matter too: a low fine with no points looks less like strict penalty and more like a model built on volume and low-cost compliance. When the cost of fighting a ticket is higher than paying it, many people will choose the path of least resistance.
The system sends the ticket to the name on the registration rather than the verified driver, and that raises constitutional questions about burden and proof. Colorado Revised Statute 42-4-110.5 recognizes that an owner might not be the driver, yet practice often presumes liability based on database matches. That flips the typical burden of proof dynamic and makes contesting a ticket a necessary step for anyone who wasn’t behind the wheel.
Proving you weren’t driving becomes an uphill task for most motorists, who lack the time, resources, or records to mount a defense. The state expects owners to disprove liability in many cases, turning due process into an administrative hurdle. That shift discourages challenges and effectively defers adjudication to whoever finds it easiest to pay.
Cameras and algorithms don’t make judgment calls. They record location and time, calculate averages, and act on thresholds without context. A human officer can account for traffic flow, weather, or immediate safety concerns; automated systems cannot. Efficiency is useful, but where nuance matters, blunt instruments can create unfair outcomes.
Beyond individual fines lies the data footprint these systems create. Tracking where a vehicle enters and exits corridors builds a movement profile that could be aggregated across networks. Stretch that across highways and cities, and you get a map of how people move, when they travel, and what routes they take.
Technology tends to expand its remit. What starts as average speed enforcement could be extended to other behaviors that are easy to digitize, like rolling stops or lane violations. Adding pattern recognition and artificial intelligence increases the range of enforceable actions and the speed at which they can be policed.
Supporters argue the system protects construction workers and improves safety, and those are valid concerns that deserve attention. But safety goals should not override core legal protections or normalise constant monitoring without robust safeguards. Public policy needs transparency, clear standards, and an easy path for people to contest automated findings.
When enforcement tools become the default, accountability can fade. The government should answer to citizens, and any system that shifts the burden onto owners and automates judgment requires scrutiny. If other states adopt similar frameworks, drivers nationwide could face a future where movement is tracked, assumptions are baked into enforcement, and due process is harder to secure.
