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Modeling Dynamic Spatio-temporal Correlations for Urban Traffic Flows Prediction

Nabeela Awan, Ahmad Ali, Fazlullah Khan, Muhammad Zakarya, Ryan Alturki, Mahwish Kundi, Mohammad Dahman Alshehri, Muhammad Haleem
2021 IEEE Access  
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

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  
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]

Stefano Fiorini, Michele Ciavotta, Andrea Maurino
2021 arXiv   pre-print
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]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
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

Rajeev Bhatt, M. Gokhan Uzunbas, Thai Hoang, Ozge C. Whiting
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
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

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
, 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]

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu
2020 arXiv   pre-print
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)  
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

Hongwei Jia, Haiyong Luo, Hao Wang, Fang Zhao, Qixue Ke, Mingyao Wu, Yunyun Zhao
2020 Sensors  
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)  
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

Mengjia Qiao, Xiaohui He, Xijie Cheng, Panle Li, Haotian Luo, Zhihui Tian, Hengliang Guo
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Rui Nian, Yu Cai, Zhengguang Zhang, Hui He, Jingyu Wu, Qiang Yuan, Xue Geng, Yuqi Qian, Hua Yang, Bo He
2021 Frontiers in Marine Science  
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

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
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

Xuan Zhou, Ruimin Ke, Hao Yang, Chenxi Liu
2021 Applied Sciences  
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

Guto Leoni Santos, Patricia Takako Endo, Djamel Sadok, Judith Kelner
2020 Algorithms  
, 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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