Urban drivers face a daily gamble: circling blocks, misreading signs, and risking tickets — just to park legally. In high-pressure environments like tourist zones, healthcare clinics, and restaurant districts, the cost isn’t just financial — it’s missed shifts, late arrivals, decision fatigue, and serious stress. Existing apps focus on static garage listings or paid lots, offering no real help in real-time, curbside parking decisions.
SlotSpot uses real-time data and predictive AI to surface the most legal, convenient parking options — just before arrival. It blends traffic cam data, smart vehicle sensors, GPS, and community input to deliver high-confidence suggestions with visual overlays and voice-ready controls. SlotSpot doesn't just show where to park — it thinks like a driver, adapts like a co-pilot, and gets better every time you use it.
I worked on this project as the sole UX and product designer, alongside a developer and with a small amount of funding, and making good use of software such as FigJam, Figma, and Illustrator. As well as copious amounts of caffeine.
Dec '24 - May '25
(6 mos. total)
I followed a structured design approach based on the Nielsen Norman Group’s Design Thinking model—moving from empathy and research to ideation, prototyping, and validation. Early in the process, I examined the national and local scope of parking stress: from urban congestion and wasted time to safety concerns and trip planning failures. These insights helped define the real-world frustrations that SlotSpot needed to solve—not just finding a space, but finding the right one.
I then conducted interviews with drivers of different backgrounds, including parents, rideshare workers, and commuters, to understand what made parking feel easy—or miserable. I mapped these findings into personas, emotional journeys, and task flows that reflected the lived experience of high-stress parking environments.
Through competitive analysis, I pinpointed gaps in existing tools: most apps lacked safety filtering, surface-level onboarding, or actionable savings insights. These pain points shaped SlotSpot’s core UX goals: reduce stress, simplify choice, and support users from driveway to destination. From there, I developed wireframes, user flows, and a responsive UI system to bring the vision to life. Everything started with empathy—and stayed grounded in the everyday frustrations of real people behind the wheel. Here's a sample of my research:
Average annual cost per driver spent searching for parking
Drivers who have avoided trips due to parking difficulties
Time the average driver spends annually searching for a spot
Drivers report feeling stress or anxiety while parking
To shape a solution grounded in real behavior, I started by identifying gaps in existing parking tools—especially their lack of emotional intelligence, real-time safety filters, and guided decision support. I backed this with targeted interviews, then synthesized the findings into journey maps and personas that captured stress, hesitation, and urgency in the moment. From there, I outlined key flows and wireframes that reflected SlotSpot’s goal: reduce cognitive load, minimize risk, and make smart parking feel simple.
Based on the research, I saw a clear opportunity: simplify legal parking decisions with real-time, safe cues, layered context, and trustworthy design to locate the best parking with easy payment methods.
Clear behavior patterns emerged from interviews, journey maps, and persona insights. Mike needed a distraction-free rerouting experience. MaryKate wanted legal curbside spots near busy zones. These needs drove SlotSpot’s core features: real-time predictive overlays, voice-first rerouting, and a smarter way to interpret legal signage.
Every design decision was tied directly to user behavior—from traffic-based AI adjustments to clean map layers and trusted legal filters. The result? A tool that doesn’t just look intelligent, but actually helps people park faster, safer, and with confidence.
Smart Sign Scan
Predictive Spot
Availability
IntegratedÂ
Payments
Map Layer Clarity
Trusted Legal
Information
Parking Zone
Notifications
To deliver smarter, more context-aware parking suggestions, SlotSpot uniquely pulls in real-time data from multiple sources: traffic cams, Tesla Vision APIs, AI-enhanced GPS, and crowdsourced driver input. These streams are filtered through predictive AI that evaluates legality, proximity, and availability in real time—adapting to your location, time of day, and car type.
This behind-the-scenes intelligence allows SlotSpot to do what a human can’t: interpret complex zone rules on the fly per situation, anticipate congestion, and recommend legal spaces faster than you could scan a block. The result? Drivers spend less time circling, second-guessing, or stressing—and more time parked exactly where they need to be. They save time, gas, vehicle wear and tear, and health issues.
This is where the concept became real. I took the insights from real drivers and shaped every screen around fast decision-making, safety, and stress relief. SlotSpot’s UI isn’t just meant to be attractive—it’s made for motion: big tap targets, glanceable info, and timely prompts that show up only when they’re needed. Each detail reflects a scenario we uncovered in interviews and journey mapping. No fluff. Just what works.
SlotSpot uses dynamic color zones to simplify decision-making in real time. As drivers approach their destination, legal and high-confidence areas are surfaced in green, while caution and restricted zones fade into yellow or red—all without overwhelming the interface.
As the driver nears the destination, SlotSpot highlights available parking zones using a real-time color system. With no taps required, the app visualizes legal options—green for high-trust areas, yellow for caution, and red for restrictions—so drivers act instantly and safely.
When the driver changes their route or updates filters mid-trip, SlotSpot instantly recalculates. The zone overlay updates in real time, offering new legal options with minimal disruption—keeping parking simple, even when life isn’t.
SlotSpot delivers only what fits—literally. With filters set, for example,to large spaces within a 5-minute walk, your smart overlay narrows the noise to show safe, spacious options nearby. Less circling, more confidence—tailored to your vehicle and time.
Unlike typical parking apps, SlotSpot delivers detailed post-drive insights—time saved, fuel conserved, and reduced stress—based on real behavior. By quantifying each success, it shows users the full impact of smarter parking decisions, reinforcing its value through data other apps simply don’t offer.
The SlotSpot prototype brings two real-world flows to life: a fast onboarding setup and a live, AI-assisted parking session. These journeys reflect SlotSpot’s mission—helping drivers find legal, available parking with clarity, confidence, and minimal stress.
The interface builds on familiar mapping tools but adds intelligence through motion-guided prompts, legality filters, and voice-friendly input. Whether en route or already parked, users get just the right amount of guidance at the right moment—from walk-distance filters to smart zone alerts. It’s a frictionless, mobile-optimized experience designed for real-time use in real driving conditions.
SlotSpot reduces the guesswork, circling, and frustration of urban parking by turning real driver behavior into timely, predictive suggestions. Its AI assistant layers data from APIs, traffic cams, and user preferences to deliver legal, personalized options—all wrapped in a calm, intuitive UI.
Testing began with remote, unmoderated usability sessions, where three users completed parking flows using the prototype. Despite limited onboarding, all testers quickly grasped the system’s intent and interacted fluidly with voice, filters, and predictive suggestions. They praised SlotSpot’s clarity, responsiveness, and tone—especially compared to clunky map-based tools.
Feedback was gathered through post-session interviews and is currently guiding refinements. Additional in-person observation testing is scheduled with three new users to evaluate performance in live mobile contexts. Some results so far:
Estimated Decision Time Reduction
Completed Tasks with Zero Confusion
Reported Increase in Parking Confidence
Said UI Felt Intuitive and Streamlined
SlotSpot’s biggest strength lies in how it cleverly fuses real-time inputs—traffic cams, GPS, Tesla Vision, and crowdsourced data—to accurately identify free and legal parking spots, not just paid garages. This multimodal orchestration makes it one of the only parking tools that helps drivers avoid fees, frustration, and wasted fuel. Testers appreciated how SlotSpot guided them clearly, felt like a co-pilot, and saved them both money and stress in real time.
Reflecting on the process, here are three things I’ll do differently moving forward:
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1. Increase the sample size and diversity of testers earlier to better validate design assumptions across different driver personas and regions.
2. Prototype deeper system logic earlier, especially for legality filters and zone transitions, to allow more thorough testing of AI-driven decisions.
3. Design analytics features up front (like savings summaries, legality logs, and zone history) rather than retrofitting them—since users responded so strongly to that data.
What’s next: With development and early funding underway, SlotSpot is entering its next phase—building out the backend, refining its real-time legality engine, and preparing for wider testing. The vision: a driver’s sidekick that not only finds smarter parking, but proves it with clear savings, fewer headaches, and zero guesswork.
Please reach out to collaborate—I'd love to hear from you!
