TMS2 - GNN Traffic Management Models
Graph Neural Network Models
GNN-based traffic state encoders for learning road network representations.
Architectures:
- GCN: Graph Convolutional Networks
- GAT: Graph Attention Networks
Use Cases:
- Traffic state encoding for RL agents
- Network-level congestion prediction
- Spatial dependency modeling
Model Description
These models are part of the Traffic Management System 2 (TMS2) project, an intelligent traffic control system using deep learning and reinforcement learning.
Training Details
- Framework: PyTorch
- Training Platform: Google Colab (T4 GPU)
- Training Date: December 2025
Usage
import torch
# Load model
model = torch.load('model.pt')
model.eval()
# Inference
with torch.no_grad():
output = model(input_tensor)
License
Apache 2.0