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

Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang, Yong Zhang
2022 Journal of Advanced Transportation  
Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance.  ...  Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task.  ...  Related Work In this section, we first provide a summary of research on graph neural networks and then give an overview of recent traffic flow forecasting research. Graph Neural Networks.  ... 
doi:10.1155/2022/2811961 fatcat:ieo4kxzsw5hbdgxhob5rnxgazy

Long-Range Transformers for Dynamic Spatiotemporal Forecasting [article]

Jake Grigsby, Zhe Wang, Yanjun Qi
2022 arXiv   pre-print
In contrast, methods based on graph neural networks explicitly model variable relationships.  ...  State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables.  ...  The code for the Spacetimeformer model was originally based on the Informer open-source release.  ... 
arXiv:2109.12218v2 fatcat:3h5jibzjzzgntegpygzh3makpm

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Pavlyuk
2020 Algorithms  
The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks.  ...  The obtained models' forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models.  ...  Acknowledgments: We thank Taek Kwon for his public archive of MnDoT traffic data [57] .  ... 
doi:10.3390/a13020039 fatcat:ofmz444gq5hhll7zn7bkm4jcoq

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

Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane Macfarlane
2020 arXiv   pre-print
It models the complex spatial and temporal dynamics of the highway network using a graph-based diffusion convolution operation within a recurrent neural network.  ...  We focus on diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art graph neural network for highway network forecasting.  ...  It is a state-of-the-art graph-based neural network that captures spatial correlation by a diffusion process on a graph and temporal dependencies using a sequence-to-sequence recurrent neural network.  ... 
arXiv:2004.08038v2 fatcat:jhligiqn2zf3xitd32hgtwjqta

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.  ...  Forecasting such events is of great importance for decision-making and resource allocation.  ...  Graph Neural Networks (GNNs) are a class of deep neural networks that can be directly applied to graphs and provide an easy way for node-level, edge-level, and graph-level prediction tasks.  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

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

Yihong Tang, Ao Qu, Andy H.F. Chow, William H.K. Lam, S.C. Wong, Wei Ma
2022 arXiv   pre-print
To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet).  ...  Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data.  ...  The authors thank the Transport Department of the Government of the Hong Kong Special Administrative Region for providing the relevant traffic data and suggestions for the experimental deployment in Hong  ... 
arXiv:2202.03630v1 fatcat:wao6svsnyzfnlijzraphy62rie

STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting [article]

Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Chenxing Wang
2021 arXiv   pre-print
Previous works combined graph convolution networks (GCNs) and self-attention mechanism with deep time series models (e.g. recurrent neural networks) to capture the spatio-temporal correlations separately  ...  In this paper, we propose a novel deep learning model for traffic forecasting, named Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which applies linear attention to the spatio-temporal  ...  In this paper, we propose a novel Multi-Context Aware Spatio-Temporal Joint Linear Attention Network (STJLA) for traffic forecasting, which design a spatio-temporal joint mode that combines the sub-graphs  ... 
arXiv:2112.02262v1 fatcat:e2bagkvdpjdu7awhyoig2pbxeu

AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting [article]

Ruikang Luo, Yaofeng Song, Liping Huang, Yicheng Zhang, Rong Su
2022 arXiv   pre-print
To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network  ...  (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data.  ...  ACKNOWLEDGMENT This study is supported under the RIE2020 Industry Alignment Fund -Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry  ... 
arXiv:2209.03356v1 fatcat:gdbngk3tabdm5losxpfptqi4g4

Neural forecasting: Introduction and literature overview [article]

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski
2020 arXiv   pre-print
Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.  ...  This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning.  ...  A look into the future Having presented an overview of the current state of NNs for forecasting, in this section we distill some open questions and promising research directions.  ... 
arXiv:2004.10240v1 fatcat:i3nbsw5sojckdee5d4magpwsl4

HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data [article]

Bang An, Amin Vahedian, Xun Zhou, W. Nick Street, Yanhua Li
2022 arXiv   pre-print
HintNet performs a multi-level spatial partitioning to separate sub-regions with different risks and learns a deep network model for each level using spatio-temporal and graph convolutions.  ...  Traffic accident forecasting is a significant problem for transportation management and public safety.  ...  Acknowledgements Bang An and Xun Zhou are partially supported by an ISSSF grant from the University of Iowa and the SAFER-SIM UTC under US-DOT award 69A3551747131.  ... 
arXiv:2203.03100v1 fatcat:624ybie5xnfq3hhlmz6zdeltpi

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Alket Cecaj, Marco Lippi, Marco Mamei, Franco Zambonelli
2021 IoT  
Finally, the article tries to identify open and promising research challenges.  ...  and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution.  ...  For example in the work presented in [110] , spatial and temporal dependencies have been modeled with Markov Random Fields and Bayesian networks for crowd flow forecasting.  ... 
doi:10.3390/iot2010003 fatcat:3iidezw7xrezthunye5ljb7yri

Real-time Forecasting of Dockless Scooter-Sharing Demand: A Context-Aware Spatio-Temporal Multi-Graph Convolutional Network Approach [article]

Yiming Xu, Mudit Paliwal, Xilei Zhao
2022 arXiv   pre-print
The proposed model applies a graph convolutional network (GCN) component that uses spatial adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity  ...  Real-time demand forecasting for shared micromobility can greatly enhance its potential benefits and mitigate its adverse effects on urban mobility.  ...  . • T-GCN: Temporal Graph Convolutional Network [39] is a spatiotemporal graph convolutional neural network that captures spatial and temporal dependency simultaneously.  ... 
arXiv:2111.01355v2 fatcat:xpn6zd7vejcmnnlnfansxujcde

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting [article]

Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
2021 arXiv   pre-print
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.  ...  It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework.  ...  For instance, DCRNN [20] incorporates both spatial and temporal dependencies in the convolutional recurrent neural network for traffic forecasting.  ... 
arXiv:2103.07719v1 fatcat:ysqgbsalfjehpojx22d2ae77gm

Traffic Congestion Forecasting in Shanghai Based on Multi-period Hotspot Clustering

Chunhui Xu, Anqin Zhang, Yu Chen
2020 IEEE Access  
With good temporal and spatial characteristics and high timeliness, these data have become important in the study of urban spatio-temporal characteristics.  ...  Traffic congestion has become increasingly prominent. Effective prediction of road congestion will provide a great reference for urban road planning and residents' travel.  ...  Toon Bogaerts et al. proposed a deep neural network that simultaneously extracts the spatial features of traffic using a graph convolution network and its temporal features by means of LSTM to make both  ... 
doi:10.1109/access.2020.2983184 fatcat:c5yvky3i7rhwdctksu672cm7m4

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Longbiao Chen, Dingqi Yang, Daqing Zhang, Cheng Wang, Jonathan Li, Thi-Mai-Trang Nguyen
2018 Journal of Network and Computer Applications  
First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for  ...  Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find  ...  Acknowledgment We would like to thank the reviewers and editors for their constructive suggestions.  ... 
doi:10.1016/j.jnca.2018.07.015 fatcat:3yyc3ikbvbfqvjjl4vkoxxlopi
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