Parking Optimizer: Building an Intelligent Parking Layout Design System with Python and AI

Author: Paolo Bechi Independent Developer & Parking Entrepreneur Florence, Tuscany, Italy

Project website: https://solitary-credit-7e47.paolo-bechi.workers.dev/

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.

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.

A parking layout is useful only when vehicles can actually reach their spaces and leave them safely.

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.

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:

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.

Imagine a parking facility with irregular surfaces, several entrances, walls, structural columns, and different types of vehicles.