From Assumption to Analytics: How Indianapolis Reimagined Parking Enforcement

The result? Uneven coverage, inefficiencies, and sometimes a sense that enforcement was arbitrary rather than fair.

In 2024, ParkIndy decided to change that. By applying data science to enforcement scheduling and deployment, Indianapolis shifted from gut instinct to measurable insight—with results that surprised even the most seasoned veterans.

The Limits of Assumption-Based Deployment

Traditional deployment patterns were built on rules of thumb:

• High meter use means more compliance and fewer citations
• Demand and parking patterns rarely change over time
• All enforcement shifts are equal

These ideas made sense—until the data showed otherwise. Traffic peaks shift, curb use evolves, and complaints don’t always align with past citation history. By leaning on assumptions, parking ambassadors sometimes spent hours patrolling quiet blocks while true hotspots with congestion and dangerous conditions went unchecked.

Worse, relying only on past tickets created a feedback loop. Streets that had always been patrolled got even more attention, while under-enforced corridors stayed under-enforced.

Replacing Anecdote with Analytics

ParkIndy built a predictive scheduling model designed to break this cycle. Instead of personal accounts and bias, the model used a blend of data sources:

• Citation history – still valuable, but carefully weighted to avoid self-fulfilling prophecy.
• Meter payment data – showing real-time demand surges and underutilized streets. Data shows the likelihood of infractions goes up as paid use increases…to a point.
• Accident and safety data – highlighting crash-prone corridors and vulnerable road user risks.
• Community complaints – ensuring lived experience factored into deployment.

These inputs generated block-by-block, time-specific likelihoods of violations, refreshed quarterly. Ambassadors then received real-time deployment maps on their phones, ensuring their patrols matched actual conditions on the ground.

Areas in red indicate a high probability of citations downtown, while those in blue represent fewer infractions. 

The Results of Data-Driven Deployment 

To build the predictive model, ParkIndy first determined the likelihood of violations by time and place, then compared those patterns to existing enforcement shifts. The gap was striking. Ambassadors were often on duty when violations were least likely, and absent during peak periods.  

Citation likelihoods (blue line), calculated with a mix of predictive data, failed to align with staffing (orange line). The late morning, mid-afternoon, and evening hours were underenforced. Mornings, on the other hand, were staffed too heavily.

Using this analysis, the team identified optimal shifts to improve performance, essentially reshaping schedules around data rather than tradition. When modeled, these optimized schedules showed a significant potential for improvement in citations per enforcement officer. 

Now parking ambassador schedules (red line) better match citation probabilities (blue line). Gaps between the likelihood of tickets and hours worked are much smaller.  

The final step was to prioritize shifts dynamically. In the past, when parking ambassadors left employment, their shifts were filled in order of departure. Dynamically prioritizing shifts ensures compliance officers are rotated to the shifts most requiring enforcement. Limited staff hours now align with the highest-impact times of day and geographies.

Shifts are prioritized to ensure the most important shifts are always worked. The top 3 shifts are indicated as 1, 2, 3 and by the red arrows.

The Results of Data-Driven Deployment

The outcomes were dramatic:

• 46% increase in citations (Jan–Aug 2025 vs. 2024)
• 96% of the increase achieved without adding staff
• Productivity per parking ambassador hour significantly improved
• Citation revenue up 45.5% year-over-year

This work wasn’t just about efficiency. By broadening the data inputs, the program reduced enforcement bias and aligned patrols with real community impacts.

Further, the program had enabled a feedback loop, allowing ParkIndy to change and reprioritize enforcement shifts as demand and the likelihood of infractions changes in the face of optimized enforcement. Following the implementation of new shifts in September, data scientists revisited performance and recommended new schedules in May. In doing so, the program ensures that parking ambassadors are always working hours and areas most in need of compliance, thereby improving their hourly productivity.

In the past, hours worked, the top line, by far exceeded productive hours, our hours issuing citations. With optimized enforcement deployment, the gap between the two, or inefficiency, has shrunk.

On the ground, safety has improved as well. With body-worn cameras and de-escalation protocols in place, altercations with the public fell 40%, allowing ambassadors to focus on service instead of conflict. During the same period, pedestrian crashes in Marion County declined 17% and fatalities dropped 54%. While causation is complex, the correlation is encouraging: optimized, evidence-based enforcement supports safer streets.

IMPO data demonstrates the collisions with vulnerable road users (VRUs) is declining.

Lessons for Other Cities

The Indianapolis experience highlights lessons for cities everywhere:

1. Challenge assumptions – Don’t accept “this is how it’s always been done.”
2. Use diverse data – Historical citation issuance alone reinforces bias. Broader datasets tell a fuller story.
3. Public safety is the foundation – Prioritizing infractions creating dangerous conditions and traffic is more defensible than increased enforcement of minor infractions.
4. Staff safety is critical – Further, predictive deployment works best if enforcement teams feel secure and supported.
5. Enforcement optimization is repeatable – Nothing prevents municipalities from optimizing enforcement to reduce congestion and dangerous conditions. By applying a feedback loop, cities can ensure constant iterative improvement.

A New Philosophy of Enforcement

Ultimately, predictive scheduling isn’t just a technical fix. It represents a shift in philosophy: from enforcement as punishment to enforcement as service.

By moving from assumptions and anecdotes to analytics and evidence, Indianapolis has shown how cities can deliver enforcement that is more efficient, more equitable, and more closely tied to community outcomes.

That’s the power of data science—not more ticket writers, but better, focused enforcement.