Urban parking demand forecasting p6#1936
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This pull request contains the complete implementation of the Urban Parking Demand Forecasting project.
Key additions:
Graph Neural Network (GCN) model for next time-step parking occupancy prediction
Spatial graph construction using parking bay proximity
Feature engineering using hour, day, latitude, and longitude
Forecasting logic using t -> t+1 occupancy labels
Area-based filtering for Richmond, Docklands, Melbourne CBD, and other regions
Interactive visualization with clickable node inspection
Final predict_by_area.py application for user-driven predictions
Updated README.md with project overview, file descriptions, setup instructions, and team contributions
Team Contributions:
Chiranjeevi: Model design, forecasting logic, pipeline integration
Aditya: Data preparation, model training, hyperparameter tuning
Aishwarya: Visualization development and UI improvements