lps20 / mit-summer-camp-mobilityai

MIT-Summer-Camp-MobilityAI

About

Our project focuses on applying large language models (LLMs) to mobility and transportation forecasting. We propose a new model called STGCN-L that incorporates LLMs into spatio-temporal graph convolutional networks for predicting future traffic conditions.

Related works

1. TCN: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
2. GLU and GTU: Language Modeling with Gated Convolutional Networks
3. ChebNet: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
4. GCN: Semi-Supervised Classification with Graph Convolutional Networks
5. STGCN: Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Related code

1. TCN: https://github.com/locuslab/TCN
2. ChebNet: https://github.com/mdeff/cnn_graph
3. GCN: https://github.com/tkipf/pygcn
4. STGCN-PyTorch: https://github.com/hazdzz/STGCN/tree/main

STGCN-L Model Structure