Graph Neural Network for Traffic Forecasting: A Survey

  title={Graph Neural Network for Traffic Forecasting: A Survey},
  author={Weiwei Jiang and Jiayun Luo},

Few-Shot Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases

A graph network (GN)-based deep learning model LOCALEGN is developed that depicts the traffic dynamics using localized data aggregating and updating functions, as well as the nodewise recurrent neural networks that outperforms existing state-of-theart baseline models.

Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting

A novel model, called Temporal Residual II Graph Convolutional Network (Tres2GCN), was proposed to capture not only multi-scale spatiotemporal but also fine-grained features.

MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

A multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model that can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively captured dynamic temporal correlations is proposed.

Traffic Prediction with Peak-Aware Temporal Graph Convolutional Networks

In this study, traffic speed prediction on a large-scale traffic network in Ankara City is performed using deep neural networks using a spatiotemporal deep learning model and the input space is expanded by temporal embedding to better take into account temporal information.

Internet traffic matrix prediction with convolutional LSTM neural network

A ConvLSTM‐based Seq2Seq model named ConvL STM‐TM is proposed for predicting the traffic matrix in the next time slot and outperforms five deep learning baselines with a lower prediction error.

DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps

A congestion-sensitive graph is constructed based on the correlations of traffic patterns, and a route-aware graph transformer is developed to directly learn the long-distance correlations of the road segments in order to improve ETA performance.

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

A continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) is proposed, allowing deeper graph propagation and fine-grained temporal information aggregation to characterize stable and precise latent spatial-temporal dynamics.

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

A deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) achieves much lower prediction error compared with the existing methods.

Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

This paper proposes a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DastNet), which is the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting prob-lems.



Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

A graph network is introduced and an optimized graph convolution recurrent neural network is proposed for traffic prediction, in which the spatial information of the road network is represented as a graph, which outperforms state-of-the-art traffic prediction methods.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

A novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state and shows that the proposed model outperforms baseline methods on two real-world traffic state datasets.

Leveraging Graph Neural Network with LSTM For Traffic Speed Prediction

  • Zhilong LuWeifeng LvZhipu XieBowen DuRunhe Huang
  • Computer Science
    2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
  • 2019
A novel graph neural network based traffic speed forecasting model, the graph Long short term Memory (GLSTM) model which consists Graph neural network (GNN) and Long shortterm Memory and the result of real world dataset shows that proposed method outperform state-of-the-art baseline methods.

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

A novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolved network (GCN), and the gated recurrent unit (GRU) to capture the spatial and temporal dependences simultaneously.

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

TL-DCRNN is developed, a new transfer learning approach for DCRNN, where a single model trained on a highway network can be used to forecast traffic on unseen highway networks, and can learn from San Francisco regional traffic data and can forecasts traffic on the Los Angeles region and vice versa.

A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting

This paper presents an auto-decomposing layer to decompose real-time traffic flow data into a stable component and a dynamic component with different spatial correlations, and proposes a two-stream graph convolutional network by considering stable and dynamic correlations in parallel.

Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network

A novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagation patterns in the graph, which reduces the computational complexity significantly from O(nm) to O(n+m), while keeping the high accuracy.

Traffic Flow Prediction Using Graph Convolution Neural Networks

  • A. Agafonov
  • Computer Science
    2020 10th International Conference on Information Science and Technology (ICIST)
  • 2020
The architecture of the graph convolution network takes into account daily and weekly patterns of traffic flow distributions and shows that the considered model outperforms other baseline forecasting algorithms.

Traffic Forecasting using Temporal Line Graph Convolutional Network: Case Study

This paper proposes a novel technique for embedding road network graphs into a Temporal-Graph Convolutional Network, and shows outstanding performance when compared to state-of-the-art techniques, not only for huge special events but also for the regular daily traffic.