相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting
Yiji Zhao et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)
Graph-based deep learning for communication networks: A survey
Weiwei Jiang
COMPUTER COMMUNICATIONS (2022)
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges
David Alexander Tedjopurnomo et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
Jiandong Bai et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2021)
Spatial Community-Informed Evolving Graphs for Demand Prediction
Qianru Wang et al.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: APPLIED DATA SCIENCE AND DEMO TRACK, ECML PKDD 2020, PT V (2021)
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu et al.
IEEE ACCESS (2021)
Short-Term Traffic Prediction With Deep Neural Networks: A Survey
Kyungeun Lee et al.
IEEE ACCESS (2021)
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Yang Zhang et al.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020)
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
Toon Bogaerts et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model
Azzedine Boukerche et al.
AD HOC NETWORKS (2020)
Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
Jinlei Zhang et al.
IET INTELLIGENT TRANSPORT SYSTEMS (2020)
Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation
Shota Fukuda et al.
IET INTELLIGENT TRANSPORT SYSTEMS (2020)
Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction
Liang Ge et al.
APPLIED SCIENCES-BASEL (2020)
Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction
Qiang Zhou et al.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2020)
Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
Byeonghyeop Yu et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
Xi Xiong et al.
TRANSPORTATION RESEARCH RECORD (2020)
Urban flow prediction from spatiotemporal data using machine learning: A survey
Peng Xie et al.
INFORMATION FUSION (2020)
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Hao Peng et al.
INFORMATION SCIENCES (2020)
An urban commuters' OD hybrid prediction method based on big GPS data
Yongdong Wang et al.
CHAOS (2020)
Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network
Zhe Chen et al.
SENSORS (2020)
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
Tanwi Mallick et al.
TRANSPORTATION RESEARCH RECORD (2020)
Traffic Prediction Using Multifaceted Techniques: A Survey
Shiju George et al.
WIRELESS PERSONAL COMMUNICATIONS (2020)
Urban Traffic Flow Forecast Based on FastGCRNN
Ya Zhang et al.
JOURNAL OF ADVANCED TRANSPORTATION (2020)
A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB
Hailong Zhu et al.
JOURNAL OF ADVANCED TRANSPORTATION (2020)
Short-term traffic speed forecasting based on graph attention temporal convolutional networks
Ge Guo et al.
NEUROCOMPUTING (2020)
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
Azzedine Boukerche et al.
COMPUTER NETWORKS (2020)
Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction
Mingming Wu et al.
2020 5TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND ARTIFICIAL INTELLIGENCE (ICMAI 2020) (2020)
Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration
Suining He et al.
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020) (2020)
Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning
Gabriela Lewenfus et al.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS (2020)
Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data
Bing He et al.
COMPLEXITY (2020)
Machine Learning-based traffic prediction models for Intelligent Transportation Systems
Azzedine Boukerche et al.
COMPUTER NETWORKS (2020)
A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention
Cong Tang et al.
IEEE ACCESS (2020)
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Ling Cai et al.
TRANSACTIONS IN GIS (2020)
A graph convolutional network model for evaluating potential congestion spots based on local urban built environments
Kun Qin et al.
TRANSACTIONS IN GIS (2020)
A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction
Jesus Mena-Oreja et al.
IEEE ACCESS (2020)
Dynamic Global-Local Spatial-Temporal Network for Traffic Speed Prediction
Dong Feng et al.
IEEE ACCESS (2020)
Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction
Ken Chen et al.
IEEE ACCESS (2020)
Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting
Weiwei Jiang et al.
TSINGHUA SCIENCE AND TECHNOLOGY (2019)
Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review
Dmitry Pavlyuk
EUROPEAN TRANSPORT RESEARCH REVIEW (2019)
A graph deep learning method for short-term traffic forecasting on large road networks
Yang Zhang et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2019)
Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks
Yong Han et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Tae San Kim et al.
PLOS ONE (2019)
Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies
Zhengchao Zhang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction
Ying Xu et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Road traffic state prediction based on a graph embedding recurrent neural network under the SCATS
Dongwei Xu et al.
CHAOS (2019)
Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder
Jamen Jian Qiao Yu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources
Shuguan Yang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism
Hongming Zhu et al.
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING (2019)
Explainability Methods for Graph Convolutional Neural Networks
Phillip E. Pope et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)
Graph Convolutional Networks for Road Networks
Tobias Skovgaard Jepsen et al.
27TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2019) (2019)
Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting
Chenhan Zhang et al.
IEEE ACCESS (2019)
A hybrid deep learning based traffic flow prediction method and its understanding
Yuankai Wu et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2018)
Predicting station-level hourly demand in a large-scale bike sharing network: A graph convolutional neural network approach
Lei Lin et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2018)
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Binbing Liao et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)
The Graph Neural Network Model
Franco Scarselli et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2009)
分享论文
