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T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction [article]

Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li
2018 arXiv   pre-print
with the graph convolutional network (GCN) and gated recurrent unit (GRU).  ...  To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination  ...  Traffic forecasting is a process of analyzing traffic conditions on urban roads, including flow, speed, and density, mining traffic patterns, and predicting the trends of traffic on roads.  ... 
arXiv:1811.05320v3 fatcat:2ih3hng7uzahxfmlxdplhtj3gu

A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System

Baozhen Yao, Ankun Ma, Rui Feng, Xiaopeng Shen, Mingheng Zhang, Yansheng Yao
2022 Frontiers in Public Health  
And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution.  ...  To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN).  ...  Based Spatial-Temporal Graph Convolutional Network, which is composed of graph convolutional layers and normal 1-D convolutional(12).  ... 
doi:10.3389/fpubh.2021.804298 pmid:35155353 pmcid:PMC8825479 fatcat:irgr4jwukfa4fdskcbs5impsqy

Traffic Message Channel prediction based on Graph Convolutional Network

Ning Li, Shuangcheng Jia, Qian Li
2021 IEEE Access  
Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM).  ...  The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle  ...  Chengjun Li for his insightful comments and technical support, thanks are due to Min Yang and Yang Chen for assistance with the manuscript editing.  ... 
doi:10.1109/access.2021.3114691 fatcat:tz3p6rdqcvedljygyl4osfm6gu

Edge Oriented Urban Hotspot Prediction for Human-centric Internet of Things

Yuanyuan Zeng, Kai Xiang
2021 IEEE Access  
CONCLUSIONS We propose a novel graph neural network based hotspot prediction framework I-GAN to detect the high traffic regions in urban city area within the human-centric IoT and MEC application scenarios  ...  best of our knowledge, I-GAN is the first graph neural network based hotspot prediction framework for urban city within MEC paradigm.  ... 
doi:10.1109/access.2021.3078479 fatcat:u3puo3fh3ncu5f5yzlybp7rbeu

Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting [article]

Jun Fu and Wei Zhou and Zhibo Chen
2020 arXiv   pre-print
In this paper, we propose a Bayesian Spatio-Temporal Graph Convolutional Network (BSTGCN) for traffic prediction.  ...  In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance.  ...  Conclusion and Future Work In this paper, we propose a Bayesian spatio-temporal graph convolutional network (BSTGCN) for traffic prediction.  ... 
arXiv:2010.07498v1 fatcat:3xujmizs2nhotnkzimvf4mmqu4

STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction [article]

Wei Zhao, Shiqi Zhang, Bing Zhou, Bei Wang
2022 arXiv   pre-print
Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations, ignoring the dynamic changes of traffic road networks  ...  The model dynamically captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data using our proposed Causal  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their helpful and constructive comments and suggestions that greatly contributed to improving the paper.  ... 
arXiv:2203.10749v1 fatcat:k5aw6iqhofdu7bep35fymp3f4m

Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction

Xiaohui Huang, Yuming Ye, Xiaofei Yang, Liyan Xiong
2022 IEEE Access  
, simultaneously, to predict traffic flow.  ...  Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more.  ...  ACKNOWLEDGMENT The authors would like to thank the editors and the anonymous reviewers whose insightful comments have helped to improve the quality of this article considerably.  ... 
doi:10.1109/access.2022.3172341 fatcat:2vgq5v5o25aknnd55pkok7djfa

Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model [chapter]

Ping Wang, Tongtong Shi, Rui He, Wubei Yuan
2022 Intelligent Electronics and Circuits - Terahertz, IRS, and Beyond [Working Title]  
In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications.  ...  Prompt and accurate prediction of traffic flow is quite useful.  ...  ., who have put a lot of energy into many formulas and illustrations. This  ... 
doi:10.5772/intechopen.101756 fatcat:fcu6fdk3avhwdc6w47wszshypy

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting [article]

Jiawei Zhu, Yujiao Song, Ling Zhao, Haifeng Li
2020 arXiv   pre-print
The A3T-GCN model learns the short-time trend in time series by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional  ...  In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations.  ...  The urban road network is constructed into a graph, and the traffic speed on roads is described as attributes of nodes on the graph.  ... 
arXiv:2006.11583v1 fatcat:n5bx23yrhrainc5krpjdptev3i

Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [article]

Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong
2021 arXiv   pre-print
Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture realtime traffic  ...  However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows.  ...  General Dynamical Graph Neural Networks Existing spectral and spatial graph convolutional networks rely on predefined graph topologies that cannot adapt to the input graph signals.  ... 
arXiv:2001.02908v2 fatcat:3dvnv5lmunaahm7k7xvkhns23e

Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

Liang Ge, Siyu Li, Yaqian Wang, Feng Chang, Kunyan Wu
2020 Applied Sciences  
To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction.  ...  The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism.  ...  Conclusions We propose a novel global spatial-temporal graph convolutional network called GSTGCN to predict urban traffic speed.  ... 
doi:10.3390/app10041509 fatcat:53fm6xiirvfbrclimg2367d7ti

Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

Haiqiang Yang, Xinming Zhang, Zihan Li, Jianxun Cui
2022 Remote Sensing  
This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU).  ...  However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges.  ...  Zhang et al. proposed a short-term traffic-flow prediction model based on a Convolution Neural Network (CNN) deep learning framework [29] .  ... 
doi:10.3390/rs14020303 fatcat:fccl5ul2cjh7pe2wyx4qbiigxq

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Jiandong Bai, Jiawei Zhu, Yujiao Song, Ling Zhao, Zhixiang Hou, Ronghua Du, Haifeng Li
2021 ISPRS International Journal of Geo-Information  
The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network.  ...  In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows.  ...  The urban road network is constructed into a graph, and the traffic speed on the roads is described as attributes of nodes on the graph.  ... 
doi:10.3390/ijgi10070485 fatcat:zobgzetux5erpj4eu3pbob7lta

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xin He, Yuben Qu, Saeed Amirgholipour, Yue Xi, Priyadarsi Nanda, Xiangjian He
2020 CCF Transactions on Pervasive Computing and Interaction  
With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control  ...  In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.  ...  Moreover, Zhao et al. (2019) presented T-GCN, a temporal graph convolutional network model that combined GCN and gated recurrent units to learn complex topological structures and predict traffic speed  ... 
doi:10.1007/s42486-020-00039-x fatcat:c3c2b3fvpzdqdlxy2ke7ckxlpu

3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting [article]

Bing Yu, Mengzhang Li, Jiyong Zhang, Zhanxing Zhu
2019 arXiv   pre-print
However, due to complicated spatio-temporal dependency and high non-linear dynamics in road networks, traffic prediction task is still challenging.  ...  In this paper, we propose a novel deep learning framework to overcome these issues: 3D Temporal Graph Convolutional Networks (3D-TGCN).  ...  Introduction Traffic speed prediction is a crucial task for many key purposes in intelligent traffic systems and urban planning.  ... 
arXiv:1903.00919v1 fatcat:277skdpx35erxkk4hamxxp4gmy
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