Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Last update: Dec 19, 2021

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Deep Learning DTMP

Overview

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

This is the implementation of traffic prediction code in DTMP based on PyTorch.

structure of the code:

data folder: storing PEMSD4 and PEMSD8 dataset. You may refer to ASTGCN for more details;
lib folder: some methods for data loading and processing from AGCRN;
utils.py: method of loading adjacency graph;
model.py: implementation of Anet;
train.py, run.py: train and run the model.

You can use python run.py --dataset PEMSD4 --num_nodes 307 command to run the code.

Owner

chenxin

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Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Related tags

Overview

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

structure of the code:

Owner

chenxin

An onlinel learning to rank python codebase.

OneFlow is a performance-centered and open-source deep learning framework.

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (EMNLP Founding 2021)

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Brain tumor detection using CNN (InceptionResNetV2 Model)

AFL binary instrumentation

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CUAD

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

nfelo: a power ranking, prediction, and betting model for the NFL

VGGVox models for Speaker Identification and Verification trained on the VoxCeleb (1 & 2) datasets

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