MS7 - Andres-acg-source/Moviles GitHub Wiki
Value Generation
To deliver on its core value proposition, ParkWise leverages both Smart Features (predictive analytics, reservation mechanisms, and visual mapping) and Context-Aware System (CAS) Features (connectivity sensing, capacity detection, and location/constraint awareness).
1. Value Generation via Smart Features
Smart features transform raw parking data into actionable, time-saving decisions throughout the user journey
| Smart Feature |
Value Generated for the User |
Operational & Value Impact |
| Occupancy Prediction Engine |
Allows users to check forecasted lot fullness before leaving home and adjust their departure time accordingly. |
Eliminates Pre-Trip Uncertainty: Converts a reactive search into a proactive decision, reducing overall arrival stress. |
| Short-Window Spot Reservation & Live Countdown |
Guarantees a specific space for a limited window, displayed via a dynamic visual countdown ring (DM Mono timer). |
Eliminates Gate Congestion: Removes circling and idling time, ensuring students arrive on time for exams and lectures. |
| Interactive SVG Level Map & Spot-Level Grid |
Renders floor layouts visually with color-coded status pills (Green = Free, Red = Occupied) without relying on heavy external CDNs. |
Reduces Decision Fatigue: Drivers visually locate open spots in seconds without reading dense lists or text labels. |
| Find My Car Wayfinding |
Stores vehicle coordinates/floor level post-park and provides step-by-step walking guidance upon return. |
Post-Trip Friction Relief: Saves time and anxiety for users returning to large, multi-level underground facilities. |
2. Value Generation via Context-Aware System (CAS) Features
CAS features dynamically adapt the application’s behavior based on environmental context, network state, capacity thresholds, and user-specific constraints:
| CAS Feature |
Context Detected |
System Adaptation & Generated Value |
| Offline & Network Degradation Sensing |
Loss of cellular signal in deep underground levels (P2 / P3). |
Zero Downtime / High Reliability: Automatically switches to offline mode using cached inline SVG maps and saved local reservations, preventing dead ends or app crashes in dead zones. |
| Capacity-Aware Conditional Flow |
Campus lot reaches 100% capacity (all levels full). |
Eliminates Dead Ends: Automatically bypasses the campus reservation flow and redirects the driver to nearby verified external lots complete with walking times to campus entrances. |
| Proximity & Distance Sensing |
Live user location relative to parking zones and campus gates. |
Optimized Wayfinding: Dynamically calculates walking distance and time from specific spots to destination buildings, prioritizing options with minimal walking effort. |
| User Constraint Awareness (Smart Filters) |
Reduced-mobility status or Electric Vehicle (EV) requirements. |
Targeted Utility: Automatically highlights accessible spots near elevators or stalls with active EV charging stations (Zap and Accessibility semantic tokens), guaranteeing equal access. |
Mockups
Link Repository iOS
Link Repository Android