🎯 The Problem
Urban areas face significant challenges with curbside space management, leading to traffic congestion, safety issues, and inefficient resource utilization. Traditional static approaches to parking and loading zone management fail to adapt to real-time demand patterns, resulting in underutilized spaces during off-peak hours and overcrowding during peak times.
💡 The Solution
Developed an AI-powered system that uses IoT sensors to monitor real-time curbside occupancy and demand patterns. The system employs machine learning algorithms to predict optimal space allocation, dynamic pricing models, and automated enforcement mechanisms. Built with Python and TensorFlow for the AI components, React for the user interface, and PostgreSQL for data management.
🚀 The Outcome
The system aims to reduce congestion by 30%, improve safety through better space utilization, and optimize urban mobility. Currently in development phase with promising initial results from simulation testing. The project addresses critical urban planning challenges and has potential for significant social impact in smart city initiatives.