research-article

Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting

Authors:

Hui XiongAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 547 - 555

https://doi.org/10.1145/3447548.3467275

Published: 14 August 2021 Publication History

Abstract

Dynamic Graph Neural Networks (DGNNs) have become one of the most promising methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed forecasting, existing approaches are usually built on a static adjacency matrix (no matter predefined or self-learned) to learn spatial relationships among different road segments, even if the impact of two road segments can be changeable dynamically during a day. Moreover, the future traffic speed cannot only be related with the current traffic speed, but also be affected by other factors such as traffic volumes. To this end, in this paper, we aim to explore these dynamic and multi-faceted spatio-temporal characteristics inherent in traffic data for further unleashing the power of DGNNs for better traffic speed forecasting. Specifically, we design a dynamic graph construction method to learn the time-specific spatial dependencies of road segments. Then, a dynamic graph convolution module is proposed to aggregate hidden states of neighbor nodes to focal nodes by message passing on the dynamic adjacency matrices. Moreover, a multi-faceted fusion module is provided to incorporate the auxiliary hidden states learned from traffic volumes with the primary hidden states learned from traffic speeds. Finally, experimental results on real-world data demonstrate that our method can not only achieve the state-of-the-art prediction performances, but also obtain the explicit and interpretable dynamic spatial relationships of road segments.

References

[1]

LEI BAI, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Advances in Neural Information Processing Systems 33 (2020).

[2]

Robert Kerwin C Billones, Argel A Bandala, Edwin Sybingco, Laurence A Gan Lim, and Elmer P Dadios. 2016. Intelligent system architecture for a vision-based contactless apprehension of traffic violations. In 2016 IEEE Region 10 Conference (TENCON). IEEE, 1871--1874.

[3]

George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons.

Digital Library

[4]

Di Chai, Leye Wang, and Qiang Yang. 2018. Bike flow prediction with multi-graph convolutional networks. In Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems. 397--400.

Digital Library

[5]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785--794.

Digital Library

[6]

Tianshu Chu, Jie Wang, Lara Codecà, and Zhaojian Li. 2019. Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Transactions on Intelligent Transportation Systems 21, 3 (2019), 1086--1095.

[7]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

Digital Library

[8]

Shengdong Du, Tianrui Li, Yan Yang, and Shi-Jinn Horng. 2019. Deep air quality forecasting using hybrid deep learning framework. IEEE Transactions on Knowledge and Data Engineering (2019).

[9]

Aleksandr Fedorov, Kseniia Nikolskaia, Sergey Ivanov, Vladimir Shepelev, and Alexey Minbaleev. 2019. Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1 (2019), 1--15.

[10]

Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 world wide web conference. 1459--1468.

Digital Library

[11]

Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2019. Predicting human mobility via variational attention. In The World Wide Web Conference. 2750--2756.

Digital Library

[12]

Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. 2019. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 3656--3663.

Digital Library

[13]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.

Digital Library

[14]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922--929.

Digital Library

[15]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.

Digital Library

[16]

Jemin Hwangbo, Inkyu Sa, Roland Siegwart, and Marco Hutter. 2017. Control of a quadrotor with reinforcement learning. IEEE Robotics and Automation Letters 2, 4 (2017), 2096--2103.

[17]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[18]

William Koch, Renato Mancuso, Richard West, and Azer Bestavros. 2019. Reinforcement learning for UAV attitude control. ACM Transactions on Cyber-Physical Systems 3, 2 (2019), 1--21.

Digital Library

[19]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.

[20]

Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, and Fei Wu. 2018. Deep sequence learning with auxiliary information for traffic prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 537--546.

Digital Library

[21]

Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2014. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems 16, 2 (2014), 865--873.

[22]

Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies 54 (2015), 187--197.

[23]

Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016).

[24]

Ledyard R Tucker. 1966. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 3 (1966), 279--311.

[25]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017).

[26]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 753--763.

Digital Library

[27]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for deep spatial-temporal graph modeling. In International Joint Conference on Artificial Intelligence 2019. Association for the Advancement of Artificial Intelligence (AAAI), 1907--1913.

[28]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5668--5675.

Digital Library

[29]

Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.

[30]

Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, and Hui Xiong. 2019. Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 305--313.

Digital Library

[31]

Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong. 2020. Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. arXiv preprint arXiv:2012.08080 (2020).

[32]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634--3640.

[33]

Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.

[34]

Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, and Jun Luo. 2020. CurbGAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 842--852.

Digital Library

[35]

Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. Gman: A graph multi-attention network for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1234--1241.

[36]

Eric Zivot and Jiahui Wang. 2006. Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus® (2006), 385--429.

Cited By

Yan YCui SLiu JZhao YZhou BKuo Y(2025)Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networksInformation Fusion10.1016/j.inffus.2024.102695114(102695)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102695
Song YLuo RZhou TZhou CSu R(2024)Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports EventsSensors10.3390/s2415479624:15(4796)Online publication date: 24-Jul-2024
https://doi.org/10.3390/s24154796
Zhang LZhu JJin BWei X(2024)Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series ForecastingSensors10.3390/s2414447324:14(4473)Online publication date: 10-Jul-2024
https://doi.org/10.3390/s24144473
Show More Cited By

Index Terms

Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

With fast proliferation of QoS-enabled wireless packet networks, need for effective QoS control is increasing. In this paper, we focus on QoS provisioning in Mobile WiMAX access service network (ASN). We investigate a dynamic bandwidth provisioning ...
Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting
Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic ...
Dynamic adaptive spatio-temporal graph neural network for multi-node offshore wind speed forecasting
Abstract
Multi-node offshore wind speed forecasting is a challenging task due to the complex dynamic spatial dependencies and highly nonlinear temporal dynamics present in the ocean. As deep learning advances, graph neural networks (GNNs) have ...
Highlights
- A dynamic spatio-temporal graph model is proposed for wind speed prediction.
- A ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

the National Natural Science Foundation of China
the Science and Technology Major Project of Beijing

Conference

KDD '21

Sponsor:

KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

110
Total Citations
View Citations
3,302
Total Downloads

Downloads (Last 12 months)820
Downloads (Last 6 weeks)61

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yan YCui SLiu JZhao YZhou BKuo Y(2025)Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networksInformation Fusion10.1016/j.inffus.2024.102695114(102695)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102695
Song YLuo RZhou TZhou CSu R(2024)Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports EventsSensors10.3390/s2415479624:15(4796)Online publication date: 24-Jul-2024
https://doi.org/10.3390/s24154796
Zhang LZhu JJin BWei X(2024)Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series ForecastingSensors10.3390/s2414447324:14(4473)Online publication date: 10-Jul-2024
https://doi.org/10.3390/s24144473
Sun YChen CXu YXie SBlum RVenkitasubramaniam P(2024)On the generalization discrepancy of spatiotemporal dynamics-informed graph convolutional networksFrontiers in Mechanical Engineering10.3389/fmech.2024.139713110Online publication date: 12-Jul-2024
https://doi.org/10.3389/fmech.2024.1397131
Yi PHuang FPeng JBao Z(2024)Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series ForecastingACM Transactions on Spatial Algorithms and Systems10.1145/3675165Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3675165
Sui HYan HZheng THuang WZhuang YLi Y(2024)Congestion-aware Spatio-Temporal Graph Convolutional Network-based A* Search Algorithm for Fastest Route SearchACM Transactions on Knowledge Discovery from Data10.1145/365764018:7(1-19)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657640
Li YGuo TQiao YBo ZWang HGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)FEST: A Multi-way Framework with Enhanced Spatial-Temporal Modeling for Traffic ForecastingProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658111(599-607)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658111
Dong ZJiang RGao HLiu HDeng JWen QSong XBaeza-Yates RBonchi F(2024)Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671961(631-641)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671961
Khoshraftar SAn A(2024)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3633518
Wang CLiang YTan GAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)CityCAN: Causal Attention Network for Citywide Spatio-Temporal ForecastingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635764(702-711)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635764
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents