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19 search results found

Bigscity Libcity ⭐ 721

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

Time Series Works Conferences ⭐ 623

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_prediction ⭐ 237

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

Spatio Temporal Papers ⭐ 194

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.

Graph_convolutional_lstm ⭐ 140

Traffic Graph Convolutional Recurrent Neural Network

Bigscity Libcity Paperlist ⭐ 132

Paper list in traffic prediction field

Research_spatio Temporal Data Mining ⭐ 126

A collection of research on spatio-temporal data mining

Baidutraffic ⭐ 125

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

Traffic Prediction Open Code Summary ⭐ 118

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

Pdformer ⭐ 98

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

Bigscity Libcity Datasets ⭐ 78

Traffic data processing tools in LibCity

Awesome Traffic Prediction ⭐ 78

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

Traffic Prediction ⭐ 42

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

Stexplainer ⭐ 15

"STExplainer: Explainable Spatio-Temporal Graph Neural Networks"

Personal Accepted Research ⭐ 12

Welcome to quote our published papers, and the codes have been uploaded.

"CL4ST: Spatio-Temporal Meta Contrastive Learning"

Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction

ALL is a pre-training method based on Federated Meta-Learning and Reinforcement Learning, which is used to address the small-sample issues of parking occupancy prediction.

Related Searches

Python Traffic Prediction (16)

Deep Learning Traffic Prediction (6)

Graph Neural Networks Traffic Prediction (6)

Dataset Traffic Prediction (6)

Network Traffic Prediction (4)

Graph Traffic Prediction (4)

Spatio Temporal Traffic Prediction (4)

Traffic Prediction Estimated Time Of Arrival (4)

Graph Convolutional Networks Traffic Prediction (4)

Pytorch Traffic Prediction (4)

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