The problem Parkour solves

Smart Parking Lot Management System

Overview

The Smart Parking Lot Management System is an intelligent, dynamic solution designed to optimize parking spaces through real-time data analysis and machine learning. This project integrates user-friendly mobile/web interfaces with a robust backend powered by FastAPI, SQLAlchemy, and advanced clustering algorithms to adjust parking prices dynamically. The system tracks parking lot occupancy and recommends price adjustments to maximize revenue and parking space utilization.

Problem Statement

Parking space management in urban areas is a growing challenge. Parking lots either get overcrowded or remain underutilized, leading to inefficient use of space and potential revenue loss. Moreover, the static pricing model does not adapt to demand fluctuations. Our system offers a dynamic solution that optimizes occupancy and improves user experience.

Key Features

1. Dynamic Price Adjustment Based on Occupancy

• The system clusters parking lots based on pricing and occupancy rates using machine learning algorithms.
• It suggests price adjustments to the frontend (without directly altering the database) based on the occupancy rates of nearby parking lots within a 5 km radius.

2. User Parking History

• Users can create accounts, park their vehicles, and have their parking history recorded.
• The system stores entry and exit times, which can be accessed via the user dashboard.

3. Parking Lot Creation and Management

• Admins can create new parking lots by specifying attributes such as latitude, longitude, capacity, scooter/car pricing, and more.
• The system supports uploading images of parking lots, enhancing the user experience.

4. Real-Time Parking Availability

• Users can find nearby parking lots within a defined radius (e.g., 5 km), filter results based on current

Challenges we ran into

Real-Time Data Synchronization: Ensuring that the system reflects real-time occupancy changes and suggests price adjustments instantly.
Clustering Algorithm Optimization: Fine-tuning the clustering algorithm to ensure accurate price adjustments based on occupancy trends and nearby competition.