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

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

Traffic Graph Convolutional Recurrent Neural Network

Some TrafficFlowForecasting Solutions(交通流量预测解决方案)

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

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

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

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

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

Codes for AAAI 2019 DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"

A PyTorch implementation of T-GCN

Attention Feature Fusion base on spatial-temporal Graph Convolutional Network（AFFGCN）

Long Short-Term Memory(LSTM) is a particular type of Recurrent Neural Network(RNN) that can retain important information over time using memory cells. This project includes understanding and implementing LSTM for traffic flow prediction along with the introduction of traffic flow prediction, Literature review, methodology, etc.

This work considers combine multi-tricks with highway network to achieve traffic flow prediction accurately.

A time series task- predicting traffic flow using LSTM model

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

Traffic Prediction using LSTM method.

Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)

ST-MAN: Spatio-Temporal Multimodal Attention Network for Traffic Prediction (KSEM 2023)