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Modeling Dynamic Spatio-temporal Correlations for Urban Traffic Flows Prediction
2021
IEEE Access
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We design a residual neural network unit for each property to depict the spatio-temporal features of traffic flows. ...
The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. ...
an attention layer with deep neural networks to model spatio-temporal dependencies. • MST3D [11] : This model applied 3D CNN to exploit citywide traffic flows prediction. • ST-ResNet [1] : This model ...
doi:10.1109/access.2021.3056926
fatcat:77vmdv3anrcitiujokl7wptcvm
Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges
2020
CCF Transactions on Pervasive Computing and Interaction
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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. ...
In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications ...
They further proposed a dynamic spatio-temporal graph convolutional neural network for traffic forecasting. ...
doi:10.1007/s42486-020-00039-x
fatcat:c3c2b3fvpzdqdlxy2ke7ckxlpu
Listening to the city, attentively: A Spatio-Temporal Attention Boosted Autoencoder for the Short-Term Flow Prediction Problem
[article]
2021
arXiv
pre-print
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In this work, we propose STREED-Net, a novel deep learning network with a multi-attention (spatial and temporal) mechanism that effectively captures and exploits complex spatial and temporal patterns in ...
However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. ...
Both branches start with a series of 3D convolutional layers used to capture the spatio-temporal dependencies among the input frames. ...
arXiv:2103.00983v3
fatcat:rbtjz4ticfbqfpqzpr34tvqy7y
Deep Learning for Spatio-Temporal Data Mining: A Survey
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1906.04928v1 fatcat:4cg55y675ba4zbx3hw3cdphdce
Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine ...
As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics based methods for dealing with such data are becoming overwhelmed ...
[97] explored the similar idea as [112] for traffic prediction on a large transportation network. [12] proposed a 3D Convolutional neural networks for citywide vehicle flow prediction. ...
arXiv:1906.04928v2
fatcat:4zrdtgkvirfuniq3rb2gl7ohpy
Segmentation of Low-Level Temporal Plume Patterns From IR Video
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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way blobs deform suggesting a need for a low-level spatio-temporal segmentation network. ...
The experiments show that plume patterns are successfully segmented out with no confusion to moving people and the proposed spatio-temporal U-Net outperforms LSTM-based network in terms of pixelwise accuracy ...
For predicting citywide crowd flows, [17] proposed a deep spatio-temporal residual network in which the input frames are grouped into distant, near and recent frames that are eventually fed through a ...
doi:10.1109/cvprw.2019.00113
dblp:conf/cvpr/BhattUHW19
fatcat:sddvfjymnnhjjnvrgvkrphhsaq
Scanning the Issue
2020
IEEE transactions on intelligent transportation systems (Print)
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, where three approximation methods, rollout method, interpolation method, and neural network, are discussed. ...
Neural network approximation usually cannot realize the effect superior to the interpolation strategy due to its complex structure, and it needs more computation time. ...
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction C. Zheng, X. Fan, C. Wen, L. Chen, C. Wang, and J. ...
doi:10.1109/tits.2020.3015408
fatcat:4yuvdgqw3fd4zp5kj7sjvkxhea
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2005.11691v5 fatcat:llygyq7jufgpzdzsaelnn3uv4iOther Versions
correlations in traffic network. ...
Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. ...
ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their valuable comments. This work is supported in part by the National Key ...
arXiv:2005.11691v6
fatcat:uiso5cg6cvhvnfmtisvuxapfqi
2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21
2020
IEEE transactions on intelligent transportation systems (Print)
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DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction. ...
., +, TITS Oct. 2020 4435-4443 Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning. ...
doi:10.1109/tits.2020.3048827
fatcat:ab6he3jkfjboxg7wa6pagbggs4
ADST: Forecasting Metro Flow using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning
2020
Sensors
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In this paper, we identify the unique spatiotemporal correlation of urban metro flow, and propose an attention-based deep spatiotemporal network with multi-task learning (ADST-Net) at a citywide level ...
Specifically, each channel uses the framework of residual networks, the rectified block and the multi-scale convolutions to mine spatiotemporal correlations. ...
Acknowledgments: Our sincere thankfulness be tendered to all the reviewers for their valuable comments and helpful suggestions.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20164574
pmid:32824074
pmcid:PMC7472615
fatcat:ynrvnnerabby5hpnqrcqesv4qa
2021 Index IEEE Transactions on Intelligent Transportation Systems Vol. 22
2021
IEEE transactions on intelligent transportation systems (Print)
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TITS May 2021 3101-3111 Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatio-Temporal Correlations. ...
., +, TITS July 2021 4519-4530 Correction to "Predicting Citywide Road Traffic Flow Using Deep Spatio-temporal Neural Networks" [May 21 3101-3111]. ...
doi:10.1109/tits.2021.3139738
fatcat:p2mkawtrsbaepj4zk24xhyl2oa
Exploiting Hierarchical Features for Crop Yield Prediction based on 3D Convolutional Neural Networks and Multi-kernel Gaussian Process
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Mokhtari, “Comparison of multiple linear regressions (mlr) and arti-
multiple 3d convolutional neural networks for citywide vehicle flow ficial ...
Zeng, “Exploiting spatio-temporal correlations with T. ...
doi:10.1109/jstars.2021.3073149
fatcat:atyweubmxjekjivs7ecruyzqru
The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly
2021
Frontiers in Marine Science
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to strengthen spatio-temporal features for long-term dependencies. ...
(MIM) for sea level anomaly (SLA) prediction, combined with the existing mesoscale eddy detection. ...
Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif. ...
doi:10.3389/fmars.2021.753942
fatcat:m4zyilfvwbf4lhzp35yttwsv2e
Scanning the Issue
2021
IEEE transactions on intelligent transportation systems (Print)
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This article proposes a logic-based traffic flow control algorithm (LB-TFC) for integrated control of ramp metering installations and variable speed limits. ...
For the first case study, LB-TFC is compared with the optimal solution and with the MTFC + PI-ALINEA algorithm. ...
Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks T. Jia and P. ...
doi:10.1109/tits.2021.3073191
fatcat:t2v7w23rmjex7eluksr46yo4v4
When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
2021
Applied Sciences
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The key challenges in ITS sensing and future directions with the integration of edge computing are discussed. ...
Given the spatial-temporal property of traffic data, the Convolutional Neural Network (CNN) is a natural choice due to its ability to learn image-like patches. ...
They built a graph CNN to exploit spatio-temporal relationships in the videos, which was able to show the relationships between different objects. ...
doi:10.3390/app11209680
fatcat:li4mubzsbncjbcqfewgr5wemeq
When 5G Meets Deep Learning: A Systematic Review
2020
Algorithms
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, user device location prediction, self network management, among others. ...
In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very ...
Acknowledgments: The authors would like to thank the Fundação de Amparo a Ciência e Tecnologia de Pernambuco (FACEPE) for funding this work through grant IBPG-0059-1.03/19. ...
doi:10.3390/a13090208
fatcat:bw3evog5xbc5jjf3bbdorda7zq
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