research-article

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

Authors:

Junbo ZhangAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

Pages 1720 - 1730

https://doi.org/10.1145/3292500.3330884

Published: 25 July 2019 Publication History

Abstract

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatio-temporal correlations, which vary from location to location and depend on the surrounding geographical information, e.g., points of interests and road networks. To tackle these challenges, we proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once. ST-MetaNet employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step. In specific, the encoder and decoder have the same network structure, consisting of a recurrent neural network to encode the traffic, a meta graph attention network to capture diverse spatial correlations, and a meta recurrent neural network to consider diverse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of ST-MetaNet beyond several state-of-the-art methods.

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Cited By

Jiang SFeng YLiao XWu HLiu JOnasanya B(2024)A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic SpeedSymmetry10.3390/sym1605053716:5(537)Online publication date: 30-Apr-2024
https://doi.org/10.3390/sym16050537
Chai WLuo QLin ZYan JZhou JZhou T(2024)Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow ForecastingSustainability10.3390/su1614586016:14(5860)Online publication date: 9-Jul-2024
https://doi.org/10.3390/su16145860
Zhang CWu YShen YWang SZhu XShen W(2024)Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow PredictionMathematics10.3390/math1210149312:10(1493)Online publication date: 10-May-2024
https://doi.org/10.3390/math12101493
Show More Cited By

Index Terms

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 25 July 2019

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Funding Sources

National Natural Science Foundation of China Grant

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '24

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The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 25 - 29, 2024

Barcelona , Spain

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Cited By

Jiang SFeng YLiao XWu HLiu JOnasanya B(2024)A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic SpeedSymmetry10.3390/sym1605053716:5(537)Online publication date: 30-Apr-2024
https://doi.org/10.3390/sym16050537
Chai WLuo QLin ZYan JZhou JZhou T(2024)Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow ForecastingSustainability10.3390/su1614586016:14(5860)Online publication date: 9-Jul-2024
https://doi.org/10.3390/su16145860
Zhang CWu YShen YWang SZhu XShen W(2024)Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow PredictionMathematics10.3390/math1210149312:10(1493)Online publication date: 10-May-2024
https://doi.org/10.3390/math12101493
Gao MWei YXie YZhang Y(2024)Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised LearningMathematics10.3390/math1209129012:9(1290)Online publication date: 24-Apr-2024
https://doi.org/10.3390/math12091290
Lu SChen HTeng Y(2024)Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for Multi-Step Traffic Flow ForecastingISPRS International Journal of Geo-Information10.3390/ijgi1303007113:3(71)Online publication date: 27-Feb-2024
https://doi.org/10.3390/ijgi13030071
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
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
https://dl.acm.org/doi/10.1145/3655629
Wu ZZheng DPan SGan QLong GKarypis G(2024)TraverseNet: Unifying Space and Time in Message Passing for Traffic ForecastingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318610335:2(2003-2013)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3186103
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