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1 T-GCN is the source codes for the paper named “T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction” published in IEEE Transactions on Intelligent Transportation Systems (T-ITS) which forged the T-GCN model the spatial and temporal dependence simultaneously. ...
解读了一下这篇论文github上关于T-GCN的代码,主要分为main文件与TGCN文件两部分,后续有空将会更新其他部分作为baseline代码的解读(鸽)。 1、main.py # -*- coding: utf-8 -*- import pickle as pkl import tensorflow as tf import pandas as pd import numpy as np import math import os import numpy....
T-GCN模型已经作为基线方法被IEEE Trans. ITS, IEEE ITS Magazine, Information science, AAAI, KDD, WWW等跨领域顶尖期刊和会议论文引用。 论文代码地址:https://github.com/lehaifeng/T-GCN. 05 参考文献 L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, H. Li*. T-GCN: ...
GCN用于学习复杂的拓扑结构以捕获空间依赖性,而GRU用于学习交通数据的动态变化以捕获时间依赖性。T-GCN的代码:GitHub - lehaifeng/T-GCN: Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method 现有的流量预测方法:自回归综合移动平均(ARIMA)模型,SVM 和部分神经网络,考虑了交通的动态变化而...
Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN. ...
flags.DEFINE_string('model_name', 'tgcn', 'tgcn or GRU or GCN.') model_name = FLAGS.model_name data_name = FLAGS.dataset train_rate = FLAGS.train_rate seq_len = FLAGS.seq_len output_dim = pre_len = FLAGS.pre_len batch_size = FLAGS.batch_size lr = FLAGS.learning_rate training...
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