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DNN-based prediction model for spatio-temporal data

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Xiuwen YiAuthors Info & Claims

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 92, Pages 1 - 4

https://doi.org/10.1145/2996913.2997016

Published: 31 October 2016 Publication History

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Abstract

Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). In this paper, we propose a Deep-learning-based prediction model for Spatio-Temporal data (DeepST). We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio-temporal and global. The spatio-temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or weekend. Using DeepST, we build a real-time crowd flow forecasting system called UrbanFlow¹. Experiment results on diverse ST datasets verify DeepST's ability to capture ST data's spatio-temporal properties, showing the advantages of DeepST beyond four baseline methods.

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Information

Published In

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 2016

649 pages

ISBN:9781450345897

DOI:10.1145/2996913

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Shawn Newsam
University of California, Merced
,
Program Chairs:
Matthias Renz
George Mason University, USA
,
Goce Trajcevski
Northwestern University, USA
,
Siva Ravada
Oracle Corporation, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31 - November 3, 2016

California, Burlingame

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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Chen TShen ZFeng SYang LKe J(2025)Dynamic matching radius decision model for on-demand ride services: A deep multi-task learning approachTransportation Research Part E: Logistics and Transportation Review10.1016/j.tre.2024.103822193(103822)Online publication date: Jan-2025
https://doi.org/10.1016/j.tre.2024.103822
Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Lu XTian FShen YZhang X(2024)Two-layer dynamic graph convolutional recurrent neural network for traffic flow predictionIntelligent Data Analysis10.3233/IDA-230174(1-17)Online publication date: 3-Jun-2024
https://doi.org/10.3233/IDA-230174
Yuan HCong GLi G(2024)Nuhuo: An Effective Estimation Model for Traffic Speed Histogram Imputation on A Road NetworkProceedings of the VLDB Endowment10.14778/3654621.365462817:7(1605-1617)Online publication date: 30-May-2024
https://doi.org/10.14778/3654621.3654628
Zhu RTang JHe XZhou XHuang XWu FChen S(2024)SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM NetworkPLOS ONE10.1371/journal.pone.031260119:12(e0312601)Online publication date: 5-Dec-2024
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Wang PLuo XTai WZhang KTrajcevsky GZhou F(2024)Score-based Graph Learning for Urban Flow PredictionACM Transactions on Intelligent Systems and Technology10.1145/365562915:3(1-25)Online publication date: 17-May-2024
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