TPnet-baseline

TPnet-baseline is a Random Forest classifier trained on smart mobility and traffic features to predict traffic congestion levels (Low, Medium, High) in urban environments.

Model Details

• Model type: Random Forest Classifier
• Input features: 20 numerical features including vehicle count, road occupancy, weather, traffic light status, time-of-day, and more
• Output: Multiclass classification – High, Medium, Low traffic congestion
• License: MIT
• Trained on: Smart Mobility Traffic Dataset from Kaggle

Training Details

• Train/test split: 80/20
• Accuracy (test): 99.9%
• F1 Score: 0.999
• Class-balanced via stratified sampling
• No overfitting observed

Evaluation

Metric	Value
Accuracy	99.9%
F1 Score	0.999
Model Size	~1.2MB

Confusion matrix and full report are available in the repository.

How to Use

import pickle

with open("traffic_predictor_rf.pkl", "rb") as f:
    model = pickle.load(f)

y_pred = model.predict(X_test)  # where X_test is a [n_samples, 20] array

Limitations

• Does not account for live data

• Designed for offline batch inference

• Assumes all 20 features are properly preprocessed and scaled

Authors

• Created by @Yukin3