Parking Optimizer: Building an Intelligent Parking Layout Design System with Python and AI
Parking Optimizer – Intelligent Parking Design Through Python, Geometry and AI
Author: Paolo Bechi
Independent Developer & Parking Entrepreneur
Florence, Tuscany, Italy
Project website:
https://solitary-credit-7e47.paolo-bechi.workers.dev/
From Real Parking Experience to Intelligent Software
My name is Paolo Bechi. I am an Italian entrepreneur working in the parking industry and an independent software developer based near Florence, Italy.
My experience in managing parking facilities has inspired me to develop a new software project called Parking Optimizer.
The idea comes from a practical question:
Can a computer learn to design parking areas more efficiently, while respecting the physical limitations of vehicles, the geometry of buildings, and the experience of professional parking operators?
This question has become the foundation of an ambitious software development project.
What Is Parking Optimizer?
Parking Optimizer is a desktop application under active development, designed to analyze parking areas and generate alternative layouts.
The objective is not simply to maximize the number of parking spaces.
The system must also consider:
- Vehicle dimensions and turning characteristics.
- Parking angles of 30°, 45°, 60°, 90°, and other configurations.
- Maneuvering corridors and access routes.
- Walls, columns, ramps, gates, and obstacles.
- Covered, uncovered, and mixed parking surfaces.
- Vehicle entry and exit feasibility.
- Comfort, accessibility, and operational efficiency.
- Existing parking arrangements that must be preserved.
A parking layout is useful only when vehicles can actually reach their spaces and leave them safely.
The Technical Architecture
The project is being developed using several technologies:
Python – Core application and optimization algorithms.
PySide6 / Qt – Desktop graphical interface.
Shapely – Computational geometry and spatial analysis.
OpenCV – Image processing and plan interpretation.
SQLite – Technical archives, vehicle profiles, benchmarks, and solution history.
Hybrid A* – Experimental vehicle path planning and maneuverability analysis.
The architecture is modular, with the aim of separating geometric calculations, vehicle behavior, document management, and learning components.
A Different Approach to Artificial Intelligence
One of the most important goals is to create a system capable of improving its optimization strategies over time.
Parking Optimizer is being designed to learn from:
- Solutions generated by its optimization engine.
- Reference layouts supplied by experienced operators.
- Geometric and maneuverability tests.
- Successful and unsuccessful optimization attempts.
- Human feedback and requested modifications.
- Technical documentation and validated design methods.
The objective is not to blindly trust AI-generated layouts.
Every proposed solution must be evaluated against measurable constraints.
Verified strategies could then become reusable knowledge for future projects.
The Parking Optimization Challenge
Imagine a parking facility with irregular surfaces, several entrances, walls, structural columns, and different types of vehicles.
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