The Strategic Shift: How We Rebuilt Ours with Risk, Not Reactions

For years, traffic enforcement at our agency felt like a never-ending cycle of chasing complaints, relying on officer hunches, and throwing patrols at hotspots long after the collisions had happened. We weren’t using data to guide our strategy—we were reacting.

Despite our best intentions, collisions kept climbing, and officers were left spinning their wheels without a clear direction. That changed with a risk-based mitigation model that didn’t just identify stale hot spots, it revealed contributing environmental factors, near-real-time risk zones, and recommended proactive deployment strategies tailored to our traffic unit.

The truth is, most agencies collect tons of traffic data – collision reports, citations, calls for service – but very few are putting that data to work. Without a strategy, enforcement becomes complaint-driven guesswork. It’s inefficient, unsustainable, and ultimately ineffective.

Why Traditional Traffic Enforcement Isn’t Working

Most agencies operate on two models:

• Complaint-based enforcement: “There are cars speeding on my street!” or “Everyone runs this stop sign.”
• Reactive Deployment: “We had a crash here, send officers out.”

Neither model accounts for long term patterns, severity, or opportunity cost. They demand a lot of effort but offer little in return and no real roadmap for prevention.

Meet the Model: The Data-Driven Traffic Triage to Traffic Enforcement

Instead of just reporting where crashes happened, we asked: Where is risk emerging—and how can we get ahead of it?

I built a monthly risk score using a weighted model that incorporated:

• Collision history (with a time-decay factor to prioritize recent trends)
• Citation data
• Day/time patterns

The result? A focused, repeatable way to deploy officers proactively – shifting from reporting to prevention.

Inside The Model: How to Prioritize Risk Over Raw Counts

One of the biggest pitfalls in traditional traffic analysis is treating every collision as equally relevant—regardless of when it happened. But we know traffic patterns shift. A crash from two years ago shouldn’t be driving today’s enforcement strategy.

That’s where time decay comes in.

In this model, each collision is assigned a risk score based on how recent it is, the more recent the crash, the higher the score; the older the crash, the lower the score. We used Excel’s exponential decay function to calculate this, so without diving too deep into formulas:

• Fewer days since the crash = higher risk
• More days = lower risk

To generate a total risk score for each intersection, we summed the individual risk scores for all crashes that occurred there. The intersections were then ranked from highest to lowest, giving us a data-driven view of emerging problem areas in near-real time.

Tools you Need (Spoiler: You Probably Already Have Them)

• Microsoft Access: For database building and automation
• Microsoft Excel: For calculations, visuals, and officer briefings
• GIS (optional but helpful): For spatial analysis and the map visuals officers engage with

The entire project was initially built out in Excel. If that’s all you have, its more than enough.

Lessons Learned (So You Don’t Have To)

• Start Simple: You don’t need perfect data, just consistent data.
• Use Time Decay: A crash from two years ago shouldn’t carry the same weight as one from yesterday.
• Officer Buy-In: If the data reflects their experience, they’ll use it.
• Visuals Matter: A simple chart beats a dense wordy report every time.

Action Steps for Your Agency

Want to replicate this in your department? Here’s a roadmap:

1. Inventory your data: Pull 3-5 years of collision and citation data.
2. Choose your weights: Assign values to each variable (recent crash, severity, type of citation).
3. Build a basic scoring model: Excel is enough to get started.
4. Visualize the results: Create a monthly or quarterly briefing for officers.
5. Track what works: Measure crash reduction, citation increase, and officer feedback.

Data Without Direction is Just Noise

You don’t need a million-dollar platform or a team of analysts to take control of your traffic data. You just need a clear goal, a repeatable process, and a bit of nerdy persistence.

This model helped us move from reactive guesswork to informed deployment—and the results are speaking for themselves.

Have questions about setting up a model like this? Want to swap tips or brainstorm strategies? Connect with me directly – I’m always happy to talk traffic. Contact me at tr******@****************tc.com.