skip to main content
10.1145/3580305.3599529acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

Published: 04 August 2023 Publication History

Abstract

Graph-based deep learning models are powerful in modeling spatio-temporal graphs for traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data, which may not be accessible in real-world applications. To address this problem, transfer learning methods are designed to transfer knowledge from the source graph with abundant data to the target graph with limited data. However, existing methods adopt pre-defined graph structures for knowledge extraction and transfer, which may be noisy or biased and negatively impact the performance of knowledge transfer. To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities. TransGTR consists of a node feature network, a structure generator, and a forecasting model. We train the node feature network with knowledge distillation to extract city-agnostic node features, such that the structure generator, taking the node features as inputs, can be transferred across both cities. Furthermore, we train the structure generator via a temporal decoupled regularization, such that the spatial features learned with the generated graphs share similar distributions across cities and thus facilitate knowledge transfer for the forecasting model. We evaluate TransGTR on real-world traffic speed datasets, where under a fair comparison, TransGTR outperforms state-of-the-art baselines by up to 5.4%.

Supplementary Material

MP4 File (280-2min-promo.mp4)
In this video, we would like to briefly introduce our work, Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities. Graph-based Traffic Forecasting is a fundamental task in smart city applications. However, without abundant traffic data, existing works fail to learn accurate forecasting models. We propose a transfer learning method for graph-based traffic forecasting, such that underdeveloped cities with limited traffic data can still obtain accurate forecasting models with knowledge transferred from data-rich cities. Our method involves joint learning of graph structures of both cities, such that both graph structures encode transferable knowledge and facilitate knowledge transfer. Experiments on real-world data show that our proposed method outperforms state-of-the-art baselines.

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, Vol. 33 (2020), 17804--17815.
[2]
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine Learning, Vol. 79, 1 (2010), 151--175.
[3]
Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola. 2006. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, Vol. 22, 14 (2006), e49--e57.
[4]
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, et al. 2020. Spectral temporal graph neural network for multivariate time-series forecasting. Advances in Neural Information Processing Systems, Vol. 33 (2020), 17766--17778.
[5]
Suresh Chavhan and Pallapa Venkataram. 2020. Prediction based traffic management in a metropolitan area. Journal of Traffic and Transportation Engineering (English edition), Vol. 7, 4 (2020), 447--466.
[6]
Paul ErdHo s, Alfréd Rényi, et al. 1960. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, Vol. 5, 1 (1960), 17--60.
[7]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, Vol. 70. PMLR, 1126--1135.
[8]
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The Journal of Machine Learning Research, Vol. 17, 1 (2016), 2096--2030.
[9]
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proc. of the AAAI Conf. on Artificial Intelligence, Vol. 33. 922--929.
[10]
David Ha, Andrew Dai, and Quoc V Le. 2016. Hypernetworks. arXiv preprint arXiv:1609.09106 (2016).
[11]
William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1025--1035.
[12]
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021. Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 547--555.
[13]
Xiaolin Han, Tobias Grubenmann, Reynold Cheng, Sze Chun Wong, Xiaodong Li, and Wenya Sun. 2020. Traffic incident detection: A trajectory-based approach. In IEEE International Conference on Data Engineering (ICDE). IEEE, 1866--1869.
[14]
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000--16009.
[15]
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, Vol. 2, 7 (2015).
[16]
Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016).
[17]
Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, and Ryosuke Shibasaki. 2021. DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4515--4525.
[18]
Weiwei Jiang and Jiayun Luo. 2022. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications (2022), 117921.
[19]
Yilun Jin, Kai Chen, and Qiang Yang. 2022. Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 731--741.
[20]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[21]
Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, and Yan Liu. 2018a. Multi-task representation learning for travel time estimation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1695--1704.
[22]
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018b. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations.
[23]
Hao Liu, Jindong Han, Yanjie Fu, Yanyan Li, Kai Chen, and Hui Xiong. 2022. Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training. The VLDB Journal (2022), 1--18.
[24]
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015. Learning transferable features with deep adaptation networks. In International Conference on Machine Learning. PMLR, 97--105.
[25]
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan. 2017. Deep transfer learning with joint adaptation networks. In International Conference on Machine Learning. PMLR, 2208--2217.
[26]
Tiancheng Lou, Jie Tang, John Hopcroft, Zhanpeng Fang, and Xiaowen Ding. 2013. Learning to predict reciprocity and triadic closure in social networks. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 7, 2 (2013), 1--25.
[27]
Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, and Xinbing Wang. 2022. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA, 1162--1172.
[28]
Hoang Nt and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 (2019).
[29]
Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, Vol. 22, 10 (2009), 1345--1359.
[30]
Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. International Conference on Learning Representations (ICLR) (2021).
[31]
Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu. 2022. Pre-Training Enhanced Spatial-Temporal Graph Neural Network for Multivariate Time Series Forecasting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA, 1567--1577.
[32]
Baochen Sun and Kate Saenko. 2016. Deep coral: Correlation alignment for deep domain adaptation. In European Conf. on Computer Vision. Springer, 443--450.
[33]
Yihong Tang, Ao Qu, Andy H.F. Chow, William H.K. Lam, S.C. Wong, and Wei Ma. 2022. Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-Term Traffic Forecasting across Cities. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. ACM, New York, USA, 1905--1915.
[34]
Luan Tran, Min Y Mun, Matthew Lim, Jonah Yamato, Nathan Huh, and Cyrus Shahabi. 2020. DeepTRANS: a deep learning system for public bus travel time estimation using traffic forecasting. Proceedings of the VLDB Endowment, Vol. 13, 12 (2020), 2957--2960.
[35]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems, Vol. 30 (2017).
[36]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. In International Conference on Learning Representations.
[37]
Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2019. Cross-city Transfer Learning for Deep Spatio-temporal Prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 1893--1899.
[38]
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019b. Simplifying graph convolutional networks. In International Conference on Machine Learning. PMLR, 6861--6871.
[39]
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.
[40]
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019a. Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 1907--1913.
[41]
Kaiqiang Xu, Xinchen Wan, Hao Wang, Zhenghang Ren, Xudong Liao, Decang Sun, Chaoliang Zeng, and Kai Chen. 2021. TACC: A full-stack cloud computing infrastructure for machine learning tasks. arXiv preprint arXiv:2110.01556 (2021).
[42]
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In The World Wide Web Conference. 2181--2191.
[43]
Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, and Hui Xiong. 2022. Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2296--2306.
[44]
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.
[45]
Haitao Yuan, Guoliang Li, Zhifeng Bao, and Ling Feng. 2021. An effective joint prediction model for travel demands and traffic flows. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 348--359.
[46]
Zhuoning Yuan, Xun Zhou, and Tianbao Yang. 2018. Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 984--992.
[47]
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan. 2019. Bridging theory and algorithm for domain adaptation. In International Conference on Machine Learning. PMLR, 7404--7413.
[48]
Bolong Zheng, Lingfeng Ming, Qi Hu, Zhipeng Lü, Guanfeng Liu, and Xiaofang Zhou. 2022. Supply-Demand-aware Deep Reinforcement Learning for Dynamic Fleet Management. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 13, 3 (2022), 1--19.
[49]
Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[50]
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui. 2021. Interpreting and unifying graph neural networks with an optimization framework. In Proceedings of the Web Conference 2021. 1215--1226.
[51]
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, and Jan Gasthaus. 2021. A study of joint graph inference and forecasting. arXiv preprint arXiv:2109.04979 (2021).

Cited By

View all
  • (2025)Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and MethodsSmart Cities10.3390/smartcities80100258:1(25)Online publication date: 7-Feb-2025
  • (2024)SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691295(338-349)Online publication date: 29-Oct-2024
  • (2024)Enhancing Dependency Dynamics in Traffic Flow Forecasting via Graph Risk BootstrapProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691237(147-159)Online publication date: 29-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
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 the author(s) 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: 04 August 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. graph structure learning
  2. traffic forecasting
  3. transfer learning

Qualifiers

  • Research-article

Funding Sources

Conference

KDD '23
Sponsor:

Acceptance Rates

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

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)904
  • Downloads (Last 6 weeks)43
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and MethodsSmart Cities10.3390/smartcities80100258:1(25)Online publication date: 7-Feb-2025
  • (2024)SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691295(338-349)Online publication date: 29-Oct-2024
  • (2024)Enhancing Dependency Dynamics in Traffic Flow Forecasting via Graph Risk BootstrapProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691237(147-159)Online publication date: 29-Oct-2024
  • (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
  • (2024)STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal ShiftsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671680(2948-2959)Online publication date: 25-Aug-2024
  • (2024)Urban Foundation Models: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671453(6633-6643)Online publication date: 25-Aug-2024
  • (2024)AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple CitiesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679856(3424-3432)Online publication date: 21-Oct-2024
  • (2024)FGITrans: Cross-City Transformer for Fine-grained Urban Flow InferenceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679855(3415-3423)Online publication date: 21-Oct-2024
  • (2024)Prompt-Based Spatio-Temporal Graph Transfer LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679554(890-899)Online publication date: 21-Oct-2024
  • (2024)Physics-Guided Multi-Source Transfer Learning for Network-Scale Traffic Flow PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340597025:11(17533-17546)Online publication date: Nov-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media