All Projects → zhiyongc → Graph_convolutional_lstm

zhiyongc / Graph_convolutional_lstm

Licence: mit
Traffic Graph Convolutional Recurrent Neural Network

Projects that are alternatives of or similar to Graph convolutional lstm

Rnn For Human Activity Recognition Using 2d Pose Input
Activity Recognition from 2D pose using an LSTM RNN
Stars: ✭ 165 (-21.43%)
Mutual labels:  jupyter-notebook, lstm
Lstm anomaly thesis
Anomaly detection for temporal data using LSTMs
Stars: ✭ 178 (-15.24%)
Mutual labels:  jupyter-notebook, lstm
Relation Classification Using Bidirectional Lstm Tree
TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
Stars: ✭ 167 (-20.48%)
Mutual labels:  jupyter-notebook, lstm
Amazon Product Recommender System
Sentiment analysis on Amazon Review Dataset available at http://snap.stanford.edu/data/web-Amazon.html
Stars: ✭ 158 (-24.76%)
Mutual labels:  jupyter-notebook, lstm
Deep Learning Random Explore
Stars: ✭ 192 (-8.57%)
Mutual labels:  jupyter-notebook, lstm
Poetry Seq2seq
Chinese Poetry Generation
Stars: ✭ 159 (-24.29%)
Mutual labels:  jupyter-notebook, lstm
Rnn For Joint Nlu
Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)
Stars: ✭ 176 (-16.19%)
Mutual labels:  jupyter-notebook, lstm
Image Caption Generator
[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow
Stars: ✭ 141 (-32.86%)
Mutual labels:  jupyter-notebook, lstm
Stylenet
A cute multi-layer LSTM that can perform like a human 🎶
Stars: ✭ 187 (-10.95%)
Mutual labels:  jupyter-notebook, lstm
Lidc nodule detection
lidc nodule detection with CNN and LSTM network
Stars: ✭ 187 (-10.95%)
Mutual labels:  jupyter-notebook, lstm
Tensorflow On Android For Human Activity Recognition With Lstms
iPython notebook and Android app that shows how to build LSTM model in TensorFlow and deploy it on Android
Stars: ✭ 157 (-25.24%)
Mutual labels:  jupyter-notebook, lstm
Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.
Stars: ✭ 199 (-5.24%)
Mutual labels:  graph, jupyter-notebook
Tensorflow Multi Dimensional Lstm
Multi dimensional LSTM as described in Alex Graves' Paper https://arxiv.org/pdf/0705.2011.pdf
Stars: ✭ 154 (-26.67%)
Mutual labels:  jupyter-notebook, lstm
Load forecasting
Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Stars: ✭ 160 (-23.81%)
Mutual labels:  jupyter-notebook, lstm
Stock Price Predictor
This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.
Stars: ✭ 146 (-30.48%)
Mutual labels:  jupyter-notebook, lstm
Deep Algotrading
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
Stars: ✭ 173 (-17.62%)
Mutual labels:  jupyter-notebook, lstm
Question Pairs Matching
第三届魔镜杯 智能客服问题相似性算法设计 第12名解决方案
Stars: ✭ 138 (-34.29%)
Mutual labels:  graph, lstm
Ethnicolr
Predict Race and Ethnicity Based on the Sequence of Characters in a Name
Stars: ✭ 137 (-34.76%)
Mutual labels:  jupyter-notebook, lstm
Graph attention pool
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
Stars: ✭ 186 (-11.43%)
Mutual labels:  graph, jupyter-notebook
Up Down Captioner
Automatic image captioning model based on Caffe, using features from bottom-up attention.
Stars: ✭ 195 (-7.14%)
Mutual labels:  jupyter-notebook, lstm

Traffic Graph Convolutional Recurrent Neural Network

A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting


Extended version of High-order Graph Convolutional Recurrent Neural Network

2nd version of the TGC-LSTM Model Structure

alt text

  • The 2nd version of the structure of Traffic Graph Convolutional LSTM (TGC-LSTM).
    • equation is the K-th order adjacency matrix
    • equation is the Free Flow Reachability matrix defined based on the network physical topology information.
  • The traffic graph convolution module is designed based on the physical network topology.
  • The code of this model is in the Code_V2 folder.
    • Environment (Jupyter Notebook): Python 3.6.1 and PyTorch 0.4.1
    • The code contains the implementations and results of the compared models, including LSTM, spectral graph convolution LSTM, localized spectral graph convolution LSTM.

1st version of the High-order Graph Convolutional Recurrent Neural Network Structure

drawing
  • The 1st version of Traffic Graph Convolutional LSTM.
  • The code of this model is in the Code_V1 folder.
    • Environment: Python 3.6.1 and PyTorch 0.3.0

Dataset

The model is tested on two real-world network-wide traffic speed dataset, loop detector data and INRIX data. The following figure shows the covered areas. (a) Seattle freeway network; (b) Seattle downtown roadway network.

drawing

Check out this Link for looking into and downloading the loop detecotr dataset. For confidentiality reasons, the INRIX dataset can not be shared.

To run the code, you need to download the loop detector data and the network topology information and put them in the proper "Data" folder.


Experimental Results

Validation Loss Comparison Chart & Model Performance with respect to the number of K

drawingdrawing

For more detailed experimental results, please refer to the paper.


Visualization

Visualization of graph convolution (GC) weight matrices (averaged, K=3) & weight values on real maps
drawing drawing

Reference

Please cite our paper if you use this code or data in your own work: Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Hope our work is benefitial for you. Thanks!

@article{cui2019traffic,
  title={Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting},
  author={Cui, Zhiyong and Henrickson, Kristian and Ke, Ruimin and Wang, Yinhai},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2019},
  publisher={IEEE}
}
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].