LibCity: An Open Library for Urban Spatial-temporal Data Mining

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

GMAN: A Graph Multi-Attention Network for Traffic Prediction (GMAN, https://fanxlxmu.github.io/publication/aaai2020/) was accepted by AAAI-2020.

Traffic Graph Convolutional Recurrent Neural Network

[TKDD 2023] Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.

Summary of open source code for deep learning models in the field of traffic prediction

[IJCAI-24] Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).

Traffic data processing tools in LibCity

Paper list in traffic prediction field

A collection of research on spatio-temporal data mining

Useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories.

Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.

HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival

[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting

[SIGKDD'2025] Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective

[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"

Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data

Predict traffic flow with LSTM. For experimental purposes only, unsupported!

[CIKM 2022] Source codes of CIKM2022 Full Paper "Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities"

[Neural Networks] RGDAN: A random graph diffusion attention network for traffic prediction

[TITS2025] Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

Traffic prediction with graph neural network using PyTorch Geometric. The implementation uses the MetaLayer class to build the GNN which allows for separate edge, node and global models.

Repository for advanced traffic forecasting models integrating GCN, LSTM/Bi-LSTM, and attention mechanisms for improved accuracy, including weather data processing.

[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"

[Neural Networks] PDG2Seq: Periodic Dynamic Graph to Sequence model for Traffic Flow Prediction

[CIKM'2024] "EasyST: A Simple Framework for Spatio-Temporal Prediction"