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bhavin273/
dynamic-parking-demand-model

Name: dynamic-parking-demand-model
Availability: InStock

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Dynamic Parking Demand Prediction Model

Model Description

This XGBoost model predicts parking demand factors based on location (H3 cells) and temporal features. It's designed for dynamic pricing systems that adjust parking rates based on predicted demand.

Model Details

Model Type: XGBoost Regressor
Task: Demand Factor Prediction (range: 1.0 - 2.0)
Framework: XGBoost 2.0.0
Python Version: 3.11
Training Date: 2026

Features Used

The model uses the following features for prediction:

Temporal Features:
- hour_sin, hour_cos: Cyclical hour encoding
- Weekday: Day of week (0-6)
- Month: Month of year (1-12)
- Quarter: Quarter of year (1-4)
- is_weekend: Weekend indicator
- isHoliday: Holiday indicator
Spatial Features:
- h3_cell_enc: Encoded H3 cell identifier
- neighbor_availability: Parking availability in neighboring areas
Trend Features:
- day_number: Days since data start
- trend_sq: Squared trend for non-linear patterns

Training Data

Data Source: Hourly parking occupancy data
Time Range: 1 year of historical data
Spatial Coverage: Multiple H3 cells (5km resolution)
Granularity: Hourly observations

Model Architecture

python

XGBRegressor(
    n_estimators=600,
    learning_rate=0.05,
    max_depth=8,
    subsample=0.9,
    colsample_bytree=0.8,
    objective="reg:squarederror"
)

Intended Use

This model is designed to:

Predict parking demand factors for dynamic pricing systems
Adjust parking rates based on predicted demand (1.0x to 2.0x base price)
Support real-time pricing decisions for parking management systems

Primary Use Cases

Smart city parking management
Dynamic pricing optimization
Parking availability forecasting
Revenue optimization for parking operators

How to Use

Via API (Deployed on Render)

bash

curl -X POST "https://your-app.onrender.com/predict" \
  -H "Content-Type: application/json" \
  -d '{
    "h3_cell": "8c2a100d1a2bfff",
    "timestamp": "2026-01-20 14:00:00"
  }'

Load Model Directly

python

import pickle
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="your-username/dynamic-parking-demand-model",
    filename="demand_prediction_model.pkl"
)

# Load model
with open(model_path, "rb") as f:
    model_data = pickle.load(f)
    model = model_data["model"]
    encoder = model_data["encoder"]
    features = model_data["features"]

Performance

Validation R²: [Add your validation score after training]
Test Set Performance: [Add test metrics]

Limitations

Requires MongoDB connection for feature retrieval in production
Limited to H3 cells present in training data
Assumes hourly time granularity
May not generalize to regions with significantly different parking patterns
Requires historical data for accurate predictions

Ethical Considerations

Fairness: The model should be monitored to ensure it doesn't create pricing disparities that unfairly affect certain demographics
Transparency: Dynamic pricing should be clearly communicated to end users
Accessibility: Consider price caps to ensure parking remains accessible

Training Details

Training Framework: scikit-learn 1.3.0, XGBoost 2.0.0
Data Preprocessing: Label encoding for categorical features, cyclical encoding for time
Validation Strategy: Per-H3-cell time-based split (last 24 hours held out)

Citation

bibtex

@software{dynamic_parking_demand_2026,
  author = {Your Name},
  title = {Dynamic Parking Demand Prediction Model},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/your-username/dynamic-parking-demand-model}
}

Contact

For questions or issues, please open an issue on the GitHub repository.

bhavin273/dynamic-parking-demand-model