Brainstorming process

Decision process

Following the divergence–convergence pattern, once the brainstorming produced our set of ideas we moved into a structured convergence process made up of three steps.

Step 1 — Post-it voting

Each member had three votes to place on any of the ideas on the board. This narrowed twelve ideas down to the handful that consistently attracted votes across different members, rather than the ones argued most loudly.

Step 2 — Four categories

We then classified the strongest surviving ideas to make sure we were not converging too early on a single type of solution:

Category	Idea	Why it landed here
The rational choice	Information system with occupancy prediction and spot reservation	Feasible with the resources and time we actually have, and buildable as a mobile app this semester.
The most likely to delight	Dedicated priority reservation module for students with reduced mobility	Solves a pain no existing alternative addresses at all, for users who currently have no option.
The darling	Student carpooling incentive platform	The idea the team personally liked most, since it attacks the root cause instead of the symptom.
The long shot	Full physical IoT sensor deployment (one sensor per spot)	Highest risk and cost by far, but it would deliver perfectly accurate data if achieved.

Step 3 — Impact vs Viability Matrix

To settle the final decision we evaluated every idea on two axes:

• Y-Axis: USER IMPACT (1 to 5) — how effectively the idea solves the key user pain points identified in the interviews, journey maps and empathy maps.
• X-Axis: IMPLEMENTATION VIABILITY (1 to 5) — technical simplicity, lower cost, and deployment speed.

quadrantChart
    title Impact vs Viability Matrix
    x-axis Low Viability 1 --> High Viability 5
    y-axis Low Impact 1 --> High Impact 5
    quadrant-1 Strategic Winners
    quadrant-2 Resource Intensive
    quadrant-3 Discarded
    quadrant-4 Quick Wins
    "Info System with Prediction": [0.82, 0.92]
    "Reduced Mobility": [0.86, 0.78]
    "Multi Story Building": [0.12, 0.92]
    "IoT Sensors per Spot": [0.32, 0.78]
    "P2P Driveway Rental": [0.22, 0.62]
    "Carpooling Platform": [0.58, 0.52]
    "Physical LED Gate Board": [0.42, 0.58]
    "Valet Parking Service": [0.25, 0.45]
    "Static Digital Map": [0.90, 0.22]
    "Student Chat Forum": [0.72, 0.35]
    "Manual Paper Tokens": [0.42, 0.18]
    "Dynamic Surge Pricing": [0.18, 0.12]

Outcome

The matrix placed the information system with prediction and reservation in the Strategic Winners quadrant with the highest combined score, alongside the reduced mobility module, which we absorbed into it as a filter rather than treating it as a separate product.

The other categories were informative but did not survive. Multi-story construction and IoT sensors per spot scored high on impact but fell into Resource Intensive: both require physical infrastructure and budget we do not control. The carpooling platform, our darling, scored mid on both axes and had already been attempted at the university without adoption. Static digital maps, the physical LED gate board and a student chat forum were also set aside: some of them already exist in one form or another, and their existence has not solved the problem, since they still leave the driver without real-time information before arriving, which is where the actual pain is.

We therefore selected the information system with prediction and reservation: it addresses the strongest pain, the uncertainty of not knowing whether there is space before driving over, while remaining feasible to build as a mobile app within the scope of the course.

Empathy maps

1. Students who don't get a parking spot in university and have to park somewhere around

2. Students who go by car everyday to university

3. Students with reduced mobility who need an accessible spot close to the entrance

4. Student who goes to university occasionally, and sometimes during off-peak hours.

5. Student who goes to university everyday on electric vehicle

Personas

Persona 2

Persona 3

Description of solution

What ParkWise is

ParkWise is the mobile solution that came out on top of our Impact vs Viability matrix: an information system with occupancy prediction and spot reservation for the parking facilities of Universidad de los Andes and the surrounding area. Instead of driving to campus hoping a spot is free, the driver opens the app and sees, in real time, how many spaces are available on each level, gets a prediction of how full the lot will be at their arrival time, and is guided to the nearest free spot.

The core idea is to remove the uncertainty that turns a five‑minute arrival into a twenty‑minute problem. Every screen is built around one question the user has every morning: "where can I park right now, and how do I get there?"

How it solves the problem

The problem we picked was the lack of parking spots at the university and the time and stress it costs students who drive every day. ParkWise attacks each pain point identified in the personas and empathy maps:

Pain point	How ParkWise addresses it
Lack of parking spots / not knowing if there is space	Real‑time availability per level, zone and individual spot, updated live.
Long waiting times and circling around	Navigation straight to the nearest free spot, so the driver stops guessing.
Arriving late because of traffic + waiting	Occupancy prediction: the user checks availability before leaving home and can plan the departure time.
Lack of security when parking outside campus	The system also surfaces availability in nearby verified lots, with walk‑time, instead of parking on the street.
Accessibility for reduced‑mobility users	Filters let the user find accessible spots close to the entrance.
Electric‑vehicle drivers	An Electric filter shows spots with charging, so EV users don't waste time.

Key features (smart features)

• Live availability dashboard – total free spaces and a capacity bar for the whole lot, plus a breakdown by level.
• Occupancy prediction – estimates how full each level will be at a chosen time, so the user decides when and where to go before driving.
• Spot‑level map – a visual layout of each floor where free spots are green and occupied ones are red, with the user's live position.
• Guided navigation – a route to the closest available spot, with an estimated walking distance from the spot to the entrance.
• Smart filters – Available, VIP, Electric (and accessible) so each user type finds exactly the spot they need.
• Reservation – hold a specific spot for a short window so it is guaranteed on arrival.
• Search – find a zone, floor or specific spot by text or voice.

Prototype walkthrough

The current prototype ("ParkWise") demonstrates the three main flows of the app:

1. Overview / home. The entry screen shows the live status of the main lot: Total Available 142 / 500 with a capacity bar, followed by the list of levels (P1 – Ground Floor / Premium Access, P2 – Underground 1 / Standard, P3 – Underground 2 / Long-term), each with its own free-spot count and a color cue (red when a level is nearly full, green when there is plenty of space). This gives the driver an at-a-glance decision in one second.

2. Level map. Selecting a level (e.g. Level P1 – North Terminal) opens a visual map of that floor. Each spot (A1–A3, C1–C3, B1–B4) is drawn as a tile: green = available, red = occupied. A bar summarizes the level (35 Available / 85 Occupied), a G / P1 / P2 selector switches floors, and a live marker shows where the driver is, with dashed guide lines toward the spots. This is where the user picks and navigates to a concrete spot.

3. Search & filter. The search view lets the user type or dictate a zone, floor or spot, and filter by All / Available / VIP / Electric. Results are shown as cards (e.g. A-42 · Zone A · L2 · Standard · 2 min walk · LIBRE), each with a GO action to start navigation; full spots are clearly marked and disabled. This flow supports users who know what kind of spot they want (closest, VIP, electric) and want the best match fast.

How the solution maps to our personas

Persona	What ParkWise gives them
Student who can't get a spot and parks around campus	Nearby verified‑lot availability with walk‑time, avoiding unsafe street parking.
Student who drives to university every day	Prediction + reservation + navigation to arrive on time without circling.
Student with reduced mobility	Filter for accessible spots close to the entrance.
Occasional / off‑peak student	Real‑time availability removes the need to learn the lot's dynamics; walk‑time helps them plan.
Everyday EV student	Electric filter to locate spots with charging quickly.

Why this solution

As documented in the decision process, Information system with prediction and reservation scored highest on both user impact and implementation viability in the Impact vs Viability matrix. It directly relieves the strongest pains (uncertainty, waiting, arriving late) while remaining technically feasible to build as a mobile app, and it avoids the drawbacks of the discarded options (physical LED boards, multi‑story construction, carpooling platforms) that were either already tried, too costly, or didn't actually solve the parking‑availability problem.