Renhe Jiang

DL-Traff-Graph. [CIKM 2021 Resource Paper] DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (Graph Part)

MegaCRN. [AAAI23] This it the official github for AAAI23 paper "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting"

DL-Traff-Grid. [CIKM 2021 Resource Paper] DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (Grid Part)

MepoGNN. [ECMLPKDD22] MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

ODCRN. [ECMLPKDD21] Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

Urban_Concept_Drift. [CIKM 2023] MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

MemeSTN. WWW23-Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

DeepCrowd. [TKDE 2021 Paper] DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

GCRN. An implementation of GCRN backbone.

CapitalTraffic. Working projects for forecasting traffic flow at accidents and incidents

DeepUrbanEvent. [ACM TIST 2021] [KDD 2019 Paper Applied Data Science Track] DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

SimpleVLUC. Python

CL-Traff. Python

TrajVis3D. C#

covid-mobility. Jupyter Notebook

Time-Series-Work-Conference. Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, etc.)

cnn-benchmarks. Benchmarks for popular CNN models

attention-is-all-you-need-pytorch. A PyTorch implementation of the Transformer model in "Attention is All You Need".

BasicTS. An Open Source Standard Time Series Forecasting Benchmark.

ST-Norm. Python

Bigscity-LibCity. LibCity: An Open Library for Traffic Prediction

STSSL_MTS. An implementation of STSSL backbone on METRLA and PEMSBAY datasets.

TwitterMobility. Jupyter Notebook

D2STGNN. Code for our VLDB'22 paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

Awesome-DynamicGraphLearning. Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).