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A Hybrid Deep Learning-based Traffic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition

Jun Cao, Xuefeng Guan, Na Zhang, Xinglei Wang, Huayi Wu
2020 IEEE Access  
On these bases, we propose a hybrid traffic speed forecasting model, flow and wavelet-integrated spatio-temporal network (FW-STN).  ...  In our study, recent and periodic dependencies are identified and used to describe the corresponding near-term and long-term effects of historical traffic data on future traffic states.  ...  Furtherly, we propose a hybrid forecasting model, flow and wavelet-integrated spatiotemporal network (FW-STN), to predict the short-term traffic speed of individual road segment.  ... 
doi:10.1109/access.2020.2991637 fatcat:mndwtworcrbn3gkxsfk2ybzvby

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 terms of the temporal factor, although there exists a tendency among adjacent time points in general, the importance of distant past points is not necessarily smaller than that of recent past points  ...  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

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

Jiawei Zhu, Qiongjie Wang, Chao Tao, Hanhan Deng, Ling Zhao, Haifeng Li
2021 IEEE Access  
Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods.  ...  Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal  ...  The prediction result of the model is similar to the changing trend of the real speed; • The performance of short-term forecasting is better than that of long-term forecasting.  ... 
doi:10.1109/access.2021.3062114 fatcat:27sptaw2xbgfnb6wt6dj4te2gu

PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction [article]

Tiange Wang, Zijun Zhang, Kwok-Leung Tsui
2021 arXiv   pre-print
Experimental results on two publicly accessible real-world urban traffic data sets show that the proposed PSTN outperforms the state-of-the-art benchmarks by significant margins for short-term traffic  ...  Finally, a multi-layer perceptron is applied to process the auxiliary road attributes and output the final predictions.  ...  ACKNOWLEDGMENT Thanks for the support of data source from Didi Chuxing GAIA Initiative.  ... 
arXiv:2108.02424v1 fatcat:sbnh7szut5hwtd7cak7ozvdtzy

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 the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also  ...  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

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

Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Ziyuan Pu, Yinhai Wang
2019 arXiv   pre-print
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.  ...  To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the  ...  × Free-flow reachable matrix by (3) ∈ ℝ Vector of speed of all graph nodes at time The short-term traffic forecasting problem aims to learn a function (•) to map time steps of historical graph signals,  ... 
arXiv:1802.07007v3 fatcat:hbe7op57bjdlhebbcieitxtzym

Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting – Full version [article]

Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang
2021 arXiv   pre-print
For example, speed sensors are deployed in different locations in a road network, where the speed of a specific location across time is captured by the corresponding sensor as a time series, resulting  ...  in multiple speed time series from different locations, which are often correlated.  ...  Traffic on different roads often has an impact of the neighbour roads. An accident on one road might cause congestion on the others.  ... 
arXiv:2103.10760v2 fatcat:k6walietajflhougakbxisw4u4

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
Then, the T-GCN model is employed to traffic forecasting based on the urban road network.  ...  However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial  ...  for short-term passenger demand forecasting.  ... 
arXiv:1811.05320v3 fatcat:2ih3hng7uzahxfmlxdplhtj3gu

STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting

Xiangyuan Kong, Weiwei Xing, Xiang Wei, Peng Bao, Jian Zhang, Wei Lu
2020 IEEE Access  
INDEX TERMS Traffic flow forecasting, spatial-temporal graph neural networks, intelligent transportation systems.  ...  Traffic flow forecasting is a critical task for urban traffic control and dispatch in the field of transportation, which is characterized by the high nonlinearity and complexity.  ...  Most prevalent approaches can perform well on short-term forecast.  ... 
doi:10.1109/access.2020.3011186 fatcat:kta4b7vy5jd6he5jtdqenyciea

A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

Hailong Zhu, Yawen Xie, Wei He, Chao Sun, Kaili Zhu, Guohui Zhou, Ning Ma, Sara Moridpour
2020 Journal of Advanced Transportation  
This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB.  ...  Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation  ...  Traffic speed is the calculated average speed of all cars on a certain road.  ... 
doi:10.1155/2020/7586154 fatcat:yfllwqmyhvdhlkbitwozbjwp2u

Space-Time Hybrid Model for Short-Time Travel Speed Prediction

Qi Fan, Wei Wang, Xiaojian Hu, Xuedong Hua, Zhuyun Liu
2018 Discrete Dynamics in Nature and Society  
Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic  ...  This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. Acknowledgments  ... 
doi:10.1155/2018/7696592 fatcat:5kkdc4pq2zdsbhph2zdzxqenbe

Graph Neural Network for Traffic Forecasting: A Survey [article]

Weiwei Jiang, Jiayun Luo
2021 arXiv   pre-print
. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms.  ...  a series of traffic forecasting problems.  ...  The benefits and disadvantages of deep learning models for short-term traffic forecasting are evaluated comprehensively, using experiments based on a variety of traffic data types, in [11] .  ... 
arXiv:2101.11174v3 fatcat:cpzxm63vlrbafipxtzy5uwp45a

Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting

Chenhan Zhang, James J. Q. Yu, Yi Liu
2019 IEEE Access  
INDEX TERMS Traffic speed prediction, graph attention, deep learning, intelligent transportation system, spatio-temporal domain feature. 166246 This work is licensed under a Creative Commons Attribution  ...  A series of comprehensive case studies on a real-world dataset demonstrate that ST-GAT supersedes existing state-of-the-art results of traffic speed prediction.  ...  TRAFFIC SPEED FORECASTING PROBLEM ON ROAD GRAPHS Traffic speed forecasting is a time-series prediction task predicting the future traffic speed, given historical traffic speed observations from sensors  ... 
doi:10.1109/access.2019.2953888 fatcat:h3mjwe3765bophjanjexw6gs24

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks [article]

Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, Xiaolei Ma
2017 arXiv   pre-print
An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.  ...  Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
arXiv:1705.02699v1 fatcat:2dkv77fmqvgcvmseajxyzcqj3y

MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting [article]

Yaobin Xu, Weitang Liu, Zhongyi Jiang, Zixuan Xu, Tingyun Mao, Lili Chen, Mingwei Zhou
2021 arXiv   pre-print
Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks.  ...  Traffic forecasting is a core element of intelligent traffic monitoring system.  ...  [19] adopted LSTM for short-term traffic forecasting. Compared to RNNs, CNNs have the advantage in calculating efficiency. Ma et al.  ... 
arXiv:2108.03594v1 fatcat:k5m6ijgmfrevtkpckmzzv6gmxm
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