ParkPredict U

ParkPredict U — real-time, AI-style smart parking guidance that points Monroe drivers to the best open lot with one tap.

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Inspiration

We all know the campus parking pain. You circle the same lots, waste 10–15 minutes, stress about being late, and still end up parking far. For a “smart” campus, it didn’t feel smart at all. So we thought: instead of guessing, what if we had a simple web app that tells you, right now, “go here, not there” for Monroe/ULM — no hardware, no paid APIs, just smart logic we can build in a few hours.

What it does

ParkPredict U looks at:

  • day of week
  • time you plan to arrive
  • which direction you’re coming from

Then it:

  • predicts how full each main lot is (A/B/C style lots),
  • highlights the best lot right now with a clean banner,
  • shows estimated free space + walk time,
  • and gives a one-click “Open in Google Maps” link straight to that lot.

It’s built as an AI-style, explainable predictor: it doesn’t just show colors, it tells you why that lot is recommended.

How we built it

  • Fully in the browser with HTML, CSS, and JavaScript.

  • Designed a lightweight “parking model” using simulated historical patterns:

    • core lots fill early,
    • overflow lots pick up later,
    • weekends and evenings calm down, etc.

  • Added a scoring layer that turns those patterns into a simple prediction for each lot.

  • Built a side panel “AI explanation” that generates a human-readable summary from the model.

  • Wired in Google Maps direction links (no paid API needed) so users can jump to navigation instantly.

  • Left a hook for the provided AI/ML key so it can call a real model later without changing the UI.

Challenges we ran into

  • Doing something that feels “real-time smart” without sensors, cameras, or a big budget.
  • Staying honest: we didn’t want to fake data, so we built a transparent model we can explain.
  • Keeping it hackathon-legal: all code started during the event, no secret backend, no expensive APIs.
  • Making the UI clean and fast enough that someone can understand it in 5 seconds during judging.

Accomplishments that we're proud of

  • A polished, deployed, zero-cost prototype that actually looks like a product, not just a demo.
  • Clear storytelling: “Here’s your best lot, here’s why, click here to go there.”
  • Campus-specific thinking for Monroe instead of a generic “AI parking” buzzword project.
  • Architecture that can plug in real data (gates, sensors, traffic) later with minimal changes.

What we learned

  • You can get a lot of “AI-feeling” usefulness just by combining good heuristics, UX, and explanations.
  • Constraints (no money, no hardware, short time) can actually push a cleaner design.
  • How important it is to show trust and transparency in smart city tools: people want to know why.

What's next for ParkPredict U

  • Hook into real Monroe/ULM data:

    • gate counters, camera-based occupancy, event schedules, weather.

  • Add live congestion inputs (when available) to refine predictions.

  • Expand to more lots + accessibility routes (priority for accessible parking & safe crossings).

  • Publish an API so other campus apps (shuttles, safety, events) can use the same predictions.

  • Turn this from “hackathon prototype” into a plug-and-play smart parking layer any campus can deploy.

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