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Curb Sense: Smarter Parking Enforcement for Safer Streets

Law Enforcement

Curb Sense: Smarter Parking Enforcement for Safer Streets

Designing a streamlined solution to help agents issue tickets efficiently and safely through automation and contextual awareness.

Project Overview

Curb Sense reimagines the parking enforcement process by replacing manual, error-prone routines with a smart assistant that automates license plate checks, identifies violations, and reduces friction for both agents and citizens. The system aims to increase accuracy, safety, and compliance by shifting the agent’s role from calculator to verifier.

Problem Statement

Parking enforcement agents still rely on manual tools, pen, paper, and a watch to issue tickets. Each city has its own set of regulations and exceptions, making it hard to maintain accuracy and consistency. Disputes are common, and in some situations, safety concerns prevent agents from issuing tickets at all. The current system is time-consuming, inefficient, and risky.

Industry

Law Enforcement

My Role

Senior User Experience Designer

Platforms

Mobile (Android)

Timeline

June 2019 - Dec 2019

Read Documentation

Read Documentation

Persona

Monique Blaitte

Parking Enforcement Agent

A municipal parking enforcement officer tasked with covering large urban areas, often alone and under time pressure.

Age: 41

Location: Agen, France

Tech Proficiency: Medium

Gender: Female

Goal

Quickly verify parking violations without doing manual checks.

Minimize confrontations and prioritize personal safety.

Ensure tickets are accurate and backed by clear, automated proof.

Frustrations

Wasting time calculating violation windows manually.

Unclear regulations across city zones and exceptions.

Paper-based processes that leave room for errors or disputes.

Process

[01] User Research

Shadowed enforcement agents in multiple municipalities to observe tools and workflows.

Reviewed legal documentation from various cities to understand regulation diversity.

Analyzed citizen dispute reports and safety incident logs from city departments.

[01] User Research

Shadowed enforcement agents in multiple municipalities to observe tools and workflows.

Reviewed legal documentation from various cities to understand regulation diversity.

Analyzed citizen dispute reports and safety incident logs from city departments.

[01] User Research

Shadowed enforcement agents in multiple municipalities to observe tools and workflows.

Reviewed legal documentation from various cities to understand regulation diversity.

Analyzed citizen dispute reports and safety incident logs from city departments.

[02] Insights

Manual processes are still widely used despite agents having access to basic digital tools.

Most enforcement agents feel vulnerable in certain neighborhoods and during night shifts.

Ticket disputes often succeed due to lack of clear evidence or inconsistent rule application.

[02] Insights

Manual processes are still widely used despite agents having access to basic digital tools.

Most enforcement agents feel vulnerable in certain neighborhoods and during night shifts.

Ticket disputes often succeed due to lack of clear evidence or inconsistent rule application.

[02] Insights

Manual processes are still widely used despite agents having access to basic digital tools.

Most enforcement agents feel vulnerable in certain neighborhoods and during night shifts.

Ticket disputes often succeed due to lack of clear evidence or inconsistent rule application.

[03] Design Solution

Centralize the process: License plate, location, and time are automatically captured and evaluated.

Integrate a regulations database per city that triggers rule-matching based on GPS and time.

Leverage a third-party API to auto-fill all license plate metadata (Make, Model, Color, Year, Owner) to ensure accuracy.

[03] Design Solution

Centralize the process: License plate, location, and time are automatically captured and evaluated.

Integrate a regulations database per city that triggers rule-matching based on GPS and time.

Leverage a third-party API to auto-fill all license plate metadata (Make, Model, Color, Year, Owner) to ensure accuracy.

[03] Design Solution

Centralize the process: License plate, location, and time are automatically captured and evaluated.

Integrate a regulations database per city that triggers rule-matching based on GPS and time.

Leverage a third-party API to auto-fill all license plate metadata (Make, Model, Color, Year, Owner) to ensure accuracy.

[04] Testing & Iteration

Developed a prototype combining GPS, clock sync, and camera functionality into one mobile interface.

Simulated ticket flows to validate speed, rule matching accuracy, and stress-free interaction.

Collected agent feedback in field tests to fine-tune the visual hierarchy and reduce cognitive load.

[04] Testing & Iteration

Developed a prototype combining GPS, clock sync, and camera functionality into one mobile interface.

Simulated ticket flows to validate speed, rule matching accuracy, and stress-free interaction.

Collected agent feedback in field tests to fine-tune the visual hierarchy and reduce cognitive load.

[04] Testing & Iteration

Developed a prototype combining GPS, clock sync, and camera functionality into one mobile interface.

Simulated ticket flows to validate speed, rule matching accuracy, and stress-free interaction.

Collected agent feedback in field tests to fine-tune the visual hierarchy and reduce cognitive load.

Outcome

Agents reduced ticket issuing time by over 50% due to automation and fewer manual inputs.
Ticket dispute rates dropped due to better documentation, auto-filled data, and photo proofs.
Agent-reported stress and risk levels decreased.

Key Learnings

Design Can Reduce Risk

By automating and speeding up critical steps, agents spend less time exposed in potentially unsafe areas.

Design Can Reduce Risk

By automating and speeding up critical steps, agents spend less time exposed in potentially unsafe areas.

Design Can Reduce Risk

By automating and speeding up critical steps, agents spend less time exposed in potentially unsafe areas.

Local Rules Need Global Systems

A regulation engine tailored per city was essential to ensure accuracy and scalability.

Local Rules Need Global Systems

A regulation engine tailored per city was essential to ensure accuracy and scalability.

Local Rules Need Global Systems

A regulation engine tailored per city was essential to ensure accuracy and scalability.

Shifting From Decision-Making to Verification

When tools do the heavy lifting, agents can focus on validating information not interpreting it under pressure.

Shifting From Decision-Making to Verification

When tools do the heavy lifting, agents can focus on validating information not interpreting it under pressure.

Shifting From Decision-Making to Verification

When tools do the heavy lifting, agents can focus on validating information not interpreting it under pressure.

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