The System Needs You to Break the Rules, And It Plans for That

Cities don’t deploy automated enforcement cameras without a spreadsheet. From the first vendor meeting to the final council vote, every rollout is backed by detailed revenue forecasts. They don’t just expect violations, they budget around them. These forecasts rarely include safety projections, but always include ticket volumes, collection rates, and net revenue. The result? A system that depends on predictable law breaking to sustain itself.

Safety in the Press Release, Revenue in the Spreadsheet

Before any city installs red light or speed cameras, a financial model is created. It may be called a cost recovery plan, a program budget, or an implementation forecast, but at its core, it’s a revenue projection. These forecasts are used to justify vendor contracts, secure council approval, and assure the public that the program won’t drain municipal funds.

What’s in a typical forecast model?

While exact formats vary, most models include:

  • Number of cameras deployed (fixed, mobile, or by location type)
  • Estimated violations per day (often based on pilot data or traffic studies)
  • Average fine amount (including surcharges and fees)
  • Collection rate (adjusted for non-payment, often 70–90%)
  • Operating costs (vendor fees, processing, redaction, staffing)
  • Net revenue estimates (gross revenue minus expenses)

Safety is rarely part of the model

Forecasts are built to project financial performance, not public outcomes. Even though camera programs are promoted as safety tools, their internal models focus on violations, collections, and recoverable costs. Metrics like “collision reduction” or “injury avoidance” are typically found in separate evaluation reports, not embedded in financial planning.

Why accuracy matters

Once fine revenue becomes part of a city’s budget planning, meeting those forecasts becomes essential. Some municipalities earmark revenue for specific safety initiatives, while others rely on it as general income. That means the enforcement system needs consistent violation volume to keep its financial assumptions on track.

Yes, cities count on violations

This is the uncomfortable truth behind automated enforcement. The system is financially healthy only if people keep breaking the rules. The fewer the violations, the smaller the return. Cities may not say it out loud, but the math reveals the logic. Noncompliance isn’t just expected, it’s forecasted.

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