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Yukin3/
TPnet-small

keras

tabular-classification

traffic

mit

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TPnet-small

TPnet-small is a lightweight deep neural network (DNN) designed to predict traffic congestion using tabular smart mobility features. It serves as a compact yet powerful alternative to tree-based models.

Model Details

Model type: Feedforward Deep Neural Network (3 hidden layers)
Architecture: [128, 64, 32] with ReLU activations and Dropout
Output: 3-class Softmax output (High, Medium, Low)
Trained on: Smart Mobility Traffic Dataset from Kaggle

Training Details

Optimizer: Adam
Loss: Sparse Categorical Crossentropy
Epochs: 50
Batch size: 32
Training time: ~6 seconds on CPU
Train Accuracy: 99.1%
Val Accuracy: 94.6%

Evaluation

Metric	Value
Accuracy	94.5%
F1 Score	0.944
Parameters	13,123
Model Size	~156 KB (.h5 format)

Includes training trajectory and confusion matrix plots.

How to Use

python

from tensorflow.keras.models import load_model

model = load_model("traffic_predictor_dnn.h5")
y_pred = model.predict(X_test)  # X_test must be scaled [n_samples, 20]

Limitations

Performance limited by small dataset size and feature coverage
Currently optimized for CPU inference, not edge deployment

Authors

Created by @Yukin3

Yukin3/TPnet-small