research-article

Spatial-Temporal Aware Inductive Graph Neural Network for C-ITS Data Recovery

Authors:

Keqin LiAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 24, Issue 8

Pages 8431 - 8442

https://doi.org/10.1109/TITS.2022.3156266

Published: 01 August 2023 Publication History

Abstract

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several different types of missing entries in spatial-temporal traffic data to meet the high-quality demand of data science applied in Cooperative-ITS (C-ITS) since accurate data recovery is critical to many downstream tasks in ITSs, such as traffic monitoring and decision making. For such, it is proposed in this article solutions to three kinds of data recovery tasks in a unified model via spatial-temporal aware Graph Neural Networks (GNNs), named Spatial-Temporal Aware Data Recovery Network (STAR), enabling a real-time and inductive inference. A residual gated temporal convolution network is designed to permit the proposed model to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation. To further exploit the generalization power of GNNs, a sampling-based method is adopted to train the proposed model to be robust and inductive for online servicing. Extensive numerical experiments on two real-world spatial-temporal traffic datasets are performed, and results show that the proposed STAR model consistently outperforms other baselines at 1.5-2.5 times on all kinds of imputation tasks. Moreover, STAR can support recovery data for 2 to 5 hours, with its performance barely unchanged, and has comparable performance in transfer learning and time-series forecast. Experimental results demonstrate that STAR provides adequate performance and rich features for multiple data recovery tasks under the C-ITS scenario.

References

[1]

W. Liang, D. Zhang, X. Lei, M. Tang, K.-C. Li, and A. Y. Zomaya, “Circuit copyright blockchain: Blockchain-based homomorphic encryption for IP circuit protection,” IEEE Trans. Emerg. Topics Comput., vol. 9, no. 3, pp. 1410–1420, Jul. 2021.

[2]

W. Liang, S. Xie, D. Zhang, X. Li, and K.-C. Li, “A mutual security authentication method for RFID-PUF circuit based on deep learning,” ACM Trans. Internet Technol., vol. 22, no. 2, pp. 1–20, May 2022.

Digital Library

[3]

Y. Wu, D. Zhuang, A. Labbe, and L. Sun, “Inductive graph neural networks for spatiotemporal Kriging,” in Proc. AAAI, 2021, pp. 4478–4485.

[4]

G. Appleby, L. Liu, and L.-P. Liu, “Kriging convolutional networks,” in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 4, pp. 3187–3194.

[5]

M. Brambilla, M. Nicoli, G. Soatti, and F. Deflorio, “Augmenting vehicle localization by cooperative sensing of the driving environment: Insight on data association in urban traffic scenarios,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 4, pp. 1646–1663, Apr. 2020.

[6]

B. Leblanc, H. Fouchal, and C. de Runz, “Obstacle detection based on cooperative-intelligent transport system data,” in Proc. IEEE Symp. Comput. Commun. (ISCC), Jul. 2020, pp. 1–6.

[7]

Y. Wu, H. Tan, Z. Jiang, and B. Ran, “ES-CTC: A deep neuroevolution model for cooperative intelligent freeway traffic control,” 2019, arXiv:1905.04083.

[8]

M. Autili, L. Chen, C. Englund, C. Pompilio, and M. Tivoli, “Cooperative intelligent transport systems: Choreography-based urban traffic coordination,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 4, pp. 2088–2099, Apr. 2021.

[9]

M. A. Javed, S. Zeadally, and E. B. Hamida, “Data analytics for cooperative intelligent transport systems,” Veh. Commun., vol. 15, pp. 63–72, Jan. 2019.

[10]

W. Liang, Y. Li, J. Xu, Z. Qin, and K.-C. Li, “QoS prediction and adversarial attack protection for distributed services under DLaaS,” IEEE Trans. Comput., to be published.

[11]

C. Chen, J. Hu, Q. Meng, and Y. Zhang, “Short-time traffic flow prediction with ARIMA-GARCH model,” in Proc. IEEE Intell. Vehicles Symp. (IV), Jun. 2011, pp. 607–612.

[12]

Y. Tian, K. Zhang, J. Li, X. Lin, and B. Yang, “LSTM-based traffic flow prediction with missing data,” Neurocomputing, vol. 318, pp. 297–305, Nov. 2018.

[13]

H. Lu, Z. Ge, Y. Song, D. Jiang, T. Zhou, and J. Qin, “A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting,” Neurocomputing, vol. 427, pp. 169–178, Feb. 2021.

[14]

Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in Proc. Int. Conf. Learn. Represent., 2018.

[15]

L. Zhao, Y. Song, C. Zhang, and Y. Liu, “T-GCN: A temporal graph convolutional network for traffic prediction,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 9, pp. 3848–3858, Sep. 2020.

[16]

B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proc. 27th Int. Joint Conf. Artif. Intell., Jul. 2018, pp. 3634–3640.

[17]

Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph WaveNet for deep spatial-temporal graph modeling,” in Proc. 28th Int. Joint Conf. Artif. Intell., Aug. 2019.

[18]

F. Li, J. Feng, H. Yan, G. Jin, D. Jin, and Y. Li, “Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,” 2021, arXiv:2104.14917.

[19]

C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, MA, USA: MIT Press, 2006.

Digital Library

[20]

N. Cressie and C. K. Wikle, Statistics for Spatio-Temporal Data. Hoboken, NJ, USA: Wiley, 2015.

[21]

J. Yoon, J. Jordon, and M. Schaar, “GAIN: Missing data imputation using generative adversarial nets,” in Proc. 35th Int. Conf. Mach. Learn., 2018, pp. 5689–5698.

[22]

W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. 31st Int. Conf. Neural Inf. Process. Syst., 2017, pp. 1025–1035.

[23]

H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna, “GraphSAINT: Graph sampling based inductive learning method,” in Proc. Int. Conf. Learn. Represent., 2020.

[24]

M. Zhang and Y. Chen, “Inductive matrix completion based on graph neural networks,” in Proc. Int. Conf. Learn. Represent., 2020.

[25]

T. Zhou, H. Shan, A. Banerjee, and G. Sapiro, “Kernelized probabilistic matrix factorization: Exploiting graphs and side information,” in Proc. SIAM Int. Conf. Data Mining, Apr. 2012, pp. 403–414.

[26]

J. Strahl, J. Peltonen, H. Mamitsuka, and S. Kaski, “Scalable probabilistic matrix factorization with graph-based priors,” in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 4, pp. 5851–5858.

[27]

D. Deng, C. Shahabi, U. Demiryurek, L. Zhu, R. Yu, and Y. Liu, “Latent space model for road networks to predict time-varying traffic,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Aug. 2016, pp. 1525–1534.

[28]

K. Takeuchi, H. Kashima, and N. Ueda, “Autoregressive tensor factorization for spatio-temporal predictions,” in Proc. IEEE Int. Conf. Data Mining (ICDM), Nov. 2017, pp. 1105–1110.

[29]

M. T. Bahadori, Q. R. Yu, and Y. Liu, “Fast multivariate spatio-temporal analysis via low rank tensor learning,” in Proc. Adv. Neural Inf. Process. Syst., vol. 27, 2014, pp. 3491–3499.

[30]

A. B. Said and A. Erradi, “Spatiotemporal tensor completion for improved urban traffic imputation,” IEEE Trans. Intell. Transp. Syst., early access, Mar. 11, 2021. 10.1109/TITS.2021.3062999.

Digital Library

[31]

X. Chen, Z. He, Y. Chen, Y. Lu, and J. Wang, “Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model,” Transp. Res. C, Emerg. Technol., vol. 104, pp. 66–77, Jul. 2019.

[32]

X. Chen, M. Lei, N. Saunier, and L. Sun, “Low-rank autoregressive tensor completion for spatiotemporal traffic data imputation,” IEEE Trans. Intell. Transp. Syst., early access, Sep. 27, 2021. 10.1109/TITS.2021.3113608.

[33]

X. Chen, Y. Chen, N. Saunier, and L. Sun, “Scalable low-rank tensor learning for spatiotemporal traffic data imputation,” Transp. Res. C, Emerg. Technol., vol. 129, Aug. 2021, Art. no.

[34]

S. Bai, J. Zico Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” 2018, arXiv:1803.01271.

[35]

Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language modeling with gated convolutional networks,” in Proc. Int. Conf. Mach. Learn., 2017, pp. 933–941.

[36]

M.-H. Guo, Z.-N. Liu, T.-J. Mu, and S.-M. Hu, “Beyond self-attention: External attention using two linear layers for visual tasks,” 2021, arXiv:2105.02358.

[37]

M. Xuet al., “Spatial-temporal transformer networks for traffic flow forecasting,” 2020, arXiv:2001.02908.

[38]

L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” in Proc. Adv. Neural Inf. Process. Syst., vol. 33, 2020, pp. 17804–17815.

[39]

Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in Proc. 26th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Aug. 2020, pp. 753–763.

[40]

A. L. Maaset al., “Rectifier nonlinearities improve neural network acoustic models,” in Proc. ICML, 2013, vol. 30, no. 1, p. 3.

[41]

J. Lei Ba, J. Ryan Kiros, and G. E. Hinton, “Layer normalization,” 2016, arXiv:1607.06450.

[42]

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent. (ICLR), San Diego, CA, USA, May 2015.

Cited By

Han DShi JZhao JWu HZhou YLi LKhan MLi K(2025)LRCNExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125658263:COnline publication date: 5-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125658
Jiang ZLuo TLiang XWooldridge MDy JNatarajan S(2024)Deep incomplete multi-view learning network with insufficient label informationProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29189(12919-12927)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29189
Li QLuo TJiang MLiao JJiang ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Deep Incomplete Multi-View Network Semi-Supervised Multi-Label Learning with Unbiased LossProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681414(9048-9056)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681414
Show More Cited By

Recommendations

Vehicle inductive signatures recognition using a Madaline neural network

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive ...
Spatial-Temporal Graph Neural Network For Interaction-Aware Vehicle Trajectory Prediction
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
In this paper, a Spatial Temporal Graph Neural Network (STGNN) model is developed, including a temporal block and Graph Neural Network (GNN) block, to solve the problem of vehicle trajectory prediction in unstructured scenes. Specifically, a temporal ...
Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction
ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

Taxi demand prediction is of much importance, which enables the building of intelligent systems and smart city. It is necessary to predict taxi demand accurately to schedule taxi fleet in a reasonable and efficient way and to reduce the pressure of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 24, Issue 8

Aug. 2023

1036 pages

ISSN:1524-9050

Issue’s Table of Contents

1558-0016 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Press

Publication History

Published: 01 August 2023

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han DShi JZhao JWu HZhou YLi LKhan MLi K(2025)LRCNExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125658263:COnline publication date: 5-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125658
Jiang ZLuo TLiang XWooldridge MDy JNatarajan S(2024)Deep incomplete multi-view learning network with insufficient label informationProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29189(12919-12927)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29189
Li QLuo TJiang MLiao JJiang ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Deep Incomplete Multi-View Network Semi-Supervised Multi-Label Learning with Unbiased LossProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681414(9048-9056)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681414
Meng XLiang WXu ZLi KKhan MKui X(2024)An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services SystemsACM Transactions on Sensor Networks10.1145/365729220:3(1-23)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3657292
Nie TQin GMa WMei YSun JBaeza-Yates RBonchi F(2024)ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal ImputationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671751(2260-2271)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671751
Wei TLin YGuo SLin YZhao YJin XWu ZWan H(2024)Inductive and adaptive graph convolution networks equipped with constraint task for spatial–temporal traffic data krigingKnowledge-Based Systems10.1016/j.knosys.2023.111325284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111325
Chen MHan LXu YZhu TWang JSun L(2024)Temporal-aware structure-semantic-coupled graph network for traffic forecastingInformation Fusion10.1016/j.inffus.2024.102339107:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102339
Xu DPeng HTang YGuo H(2024)Hierarchical spatio-temporal graph convolutional neural networks for traffic data imputationInformation Fusion10.1016/j.inffus.2024.102292106:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102292
Hu NZhang DXie KLiang WLi KZomaya A(2024)Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow predictionFuture Generation Computer Systems10.1016/j.future.2024.04.052158:C(323-332)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.052
Hu XLiu WHuo H(2024)An intelligent network traffic prediction method based on Butterworth filter and CNN–LSTMComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110172240:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110172
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents