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Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [article]

Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng
2021 arXiv   pre-print
To tackle these challenges, we develop a new traffic prediction framework-Spatial-Temporal Graph Diffusion Network (ST-GDN).  ...  Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics.  ...  Acknowledgments The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions.  ... 
arXiv:2110.04038v1 fatcat:o5httsdvevewvmc43nwplp7hj4

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To tackle these challenges, we develop a new traffic prediction framework–Spatial-Temporal Graph Diffusion Network (ST-GDN).  ...  Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics.  ...  Acknowledgments The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions.  ... 
doi:10.1609/aaai.v35i17.17761 fatcat:74gvheyvnjdelfdefl7yye2t3q

Urban flows prediction from spatial-temporal data using machine learning: A survey [article]

Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang
2019 arXiv   pre-print
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.  ...  Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task.  ...  A novel network for spatial-temporal prediction with region representation was developed [77] , as shown in Figure 8 .  ... 
arXiv:1908.10218v1 fatcat:yzt6qe4oxnczzmklyirxkpkw7q

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays.  ...  learning tasks due to their powerful hierarchical feature learning ability in both spatial and temporal domains, and have been widely applied in various spatio-temporal data mining (STDM) tasks such as  ...  Specifically, in the first level attention, a spatial attention mechanism consisting of local spatial attention and global spatial attention is proposed to capture the complex spatial correlations between  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation [article]

Zhiwen Zhang, Hongjun Wang, Jiyuan Chen, Zipei Fan, Xuan Song, Ryosuke Shibasaki
2022 arXiv   pre-print
To address the above challenges, we introduce GOF-TTE for the mobile user group, Generative Online Federated Learning Framework for Travel Time Estimation, which I) utilizes the federated learning approach  ...  model prediction. % III) designs the global model as an online generative model shared by all clients to infer the real-time road traffic state.  ...  For example, ConSTGAT [17] employs a graph attention mechanism onto the spatial-temporal features by integrating traffic and contextual information, in which the input feature includes the road segment-based  ... 
arXiv:2207.00838v1 fatcat:xwbktwzaxjf3vdpb3injjcv4dm

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction [article]

Zhonghang Li and Chao Huang and Lianghao Xia and Yong Xu and Jian Pei
2022 arXiv   pre-print
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.  ...  Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation  ...  ACKNOWLEDGMENTS We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2204.08587v2 fatcat:fdjwlnuqjfhjxhpwxj5r7sysva

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS Jan. 2020 285-297 T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction.  ...  ., +, TITS July 2020 3046-3055 T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction.  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

Deep learning for Network Traffic Monitoring and Analysis (NTMA): A survey

Mahmoud Abbasi, Amin Shahraki, Amir Taherkordi
2021 Computer Communications  
Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction  ...  Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity.  ...  Zhang et al. [133] Citywide traffic prediction CNN Applying CNN to model spatial and temporal dependence of traffic in different cells.  ... 
doi:10.1016/j.comcom.2021.01.021 fatcat:hreufl5lybhtlhn4otilhzznpq

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

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions.  ...  We first introduce how event forecasting problems are formulated as a machine learning prediction task.  ...  They studied the effect of noises (i.e., local outliers and irregular waves) on crime data and proposed a Dual-robust Enhanced Spatial-temporal Learning Network (DuroNet) [52] with an encoder-decoder  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

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 Oct. 2021 6175-6187 A Spatial-Temporal Attention Approach for Traffic Prediction.  ...  ., +, TITS Nov. 2021 7004-7014 Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction.  ... 
doi:10.1109/tits.2021.3139738 fatcat:p2mkawtrsbaepj4zk24xhyl2oa

Enhanced Air Quality Inference via Multi-view Learning with Mobile Sensing Memory

Ning Liu, Xinyu Liu, Po-Ting Lin, Yue Wang, Lin Zhang
2022 IEEE Access  
To address these challenges, we propose AQI-M 3 , a novel framework for fine-grained air quality inference via multi-view learning with mobile sensing memory.  ...  In addition, a memory network is designed to capture the spatial patterns from the historical sensing data and provide the global patterns as a complemental guide to overcome dynamic coverage sampling.  ...  ACKNOWLEDGMENT The authors would like to thank Shenzhen Environmental Thinking Science and Technology (ETST) Company Ltd. for their assistance in system deployment and data collection.  ... 
doi:10.1109/access.2022.3164506 fatcat:bn5bakzkt5hjvngon2zjlmgauu

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation [article]

Lingbo Liu, Yuying Zhu, Guanbin Li, Ziyi Wu, Lei Bai, Liang Lin
2022 arXiv   pre-print
Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction.  ...  branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal  ...  [40] introduced attention mechanisms into spatial-temporal graph networks for dynamical traffic prediction. Bai et al.  ... 
arXiv:2107.00946v5 fatcat:glgtdvkjjfh2pofjumzi7wxpo4

Location prediction on trajectory data: A review

Ruizhi Wu, Guangchun Luo, Junming Shao, Ling Tian, Chengzong Peng
2018 Big Data Mining and Analytics  
Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction.  ...  Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few.  ...  Acknowledgment We thank the editors and reviewer for everything you have done for us.  ... 
doi:10.26599/bdma.2018.9020010 dblp:journals/bigdatama/WuLSTP18 fatcat:3ogap5xsxffjxazjm7chcnqu3u

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science [article]

Huimin Chen, Cheng Yang, Xuanming Zhang, Zhiyuan Liu, Maosong Sun, Jianbin Jin
2021 arXiv   pre-print
To explore the answer, we give a thorough review of data representations in CSS for both text and network.  ...  The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation.  ...  [288] used graph attention network to encode road networks, and RNN to further embed the temporal sequence of traffics for urban traffic prediction.  ... 
arXiv:2106.14198v1 fatcat:dvy5awnfuvbnnkzusjl5wbhfki

Fine-Grained Urban Flow Inference [article]

Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng,, David S. Rosenblum
2020 arXiv   pre-print
This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts.  ...  To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a  ...  [39] to simultaneously model spatial dependencies (both near and distant), and temporal dynamics of various scales (i.e., closeness, period and trend) for citywide crowd flow prediction.  ... 
arXiv:2002.02318v1 fatcat:l2jkjhjx2bavzaarcm4sxbdsdu
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