Cited By
View all- Liu YWu XTang YLi XSun DZheng L(2024)Decomposition with feature attention and graph convolution network for traffic forecastingKnowledge-Based Systems10.1016/j.knosys.2024.112193300(112193)Online publication date: Oct-2024
Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Pages 1451 - 1460
Abstract
Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.
References
[1]
Yasunori Akagi, Takuya Nishimura, Takeshi Kurashima, and Hiroyuki Toda. 2018. A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data. In IJCAI. 3293--3300.
[2]
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. arXiv preprint arXiv:2007.02842 (2020).
[3]
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 33 (2020), 17766--17778.
[4]
Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. 2022. Graph neural controlled differential equations for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 6367--6374.
[5]
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan. 2022. Towards spatio-temporal aware traffic time series forecasting. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2900--2913.
[6]
Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. 2019. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 890--897.
[7]
GAIA initiative Didi. 2020. Didi Chuxing data. https://gaia.didichuxing.com.
[8]
Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang. 2021. Adarnn: Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 402--411.
[9]
Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2019. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. In IJCAI. 2286--2293.
[10]
Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie. 2021. Spatialtemporal graph ode networks for traffic flow forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 364--373.
[11]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In International conference on machine learning. PMLR, 1126--1135.
[12]
Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. 2021. Hierarchical Graph Convolution Network for Traffic Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 151--159.
[13]
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.
[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]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.
[16]
Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, and Weiyang Kong. 2020. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. In IJCAI. 2355--2361.
[17]
Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang. 2022. STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction. (2022).
[18]
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.
[19]
Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[20]
Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. 2022. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In International Conference on Machine Learning. PMLR, 11906--11917.
[21]
Hyunwook Lee, Seungmin Jin, Hyeshin Chu, Hongkyu Lim, and Sungahn Ko. 2021. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. arXiv preprint arXiv:2110.10380 (2021).
[22]
Hung-yi Lee, Shang-Wen Li, and Ngoc Thang Vu. 2022. Meta Learning for Natural Language Processing: A Survey. arXiv preprint arXiv:2205.01500 (2022).
[23]
Mengzhang Li and Zhanxing Zhu. 2021. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 4189--4196.
[24]
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017).
[25]
Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Yuhao Du, Fuliang Li, and Zhenhui Li. 2022. Cblab: Scalable traffic simulation with enriched data supporting. arXiv preprint arXiv:2210.00896 (2022).
[26]
Marco Lippi, Matteo Bertini, and Paolo Frasconi. 2013. Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intelligent Transportation Systems 14, 2 (2013), 871--882.
[27]
Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, and Roger Zimmermann. 2023. Do We Really Need Graph Neural Networks for Traffic Forecasting? arXiv:2301.12603 [cs.LG]
[28]
Zhanyu Liu, Chumeng Liang, Guanjie Zheng, and Hua Wei. 2023. FDTI: Finegrained Deep Traffic Inference with Roadnet-enriched Graph. arXiv preprint arXiv:2306.10945 (2023).
[29]
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. arXiv preprint arXiv:2205.13947 (2022).
[30]
Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order metalearning algorithms. arXiv preprint arXiv:1803.02999 (2018).
[31]
Ali Yadavar Nikravesh, Samuel A Ajila, Chung-Horng Lung, and Wayne Ding. 2016. Mobile network traffic prediction using MLP, MLPWD, and SVM. In 2016 IEEE International Congress on Big Data (BigData Congress). IEEE, 402--409.
[32]
Iwao Okutani and Yorgos J Stephanedes. 1984. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological 18, 1 (1984), 1--11.
[33]
Boris N Oreshkin, Arezou Amini, Lucy Coyle, and Mark Coates. 2021. FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 9233--9241.
[34]
Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 1720--1730.
[35]
Xuan Rao, Hao Wang, Liang Zhang, Jing Li, Shuo Shang, and Peng Han. 2022. Fogs: First-order gradient supervision with learning-based graph for traffic flow forecasting. In Proceedings of International Joint Conference on Artificial Intelligence, IJCAI. ijcai. org.
[36]
Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53--65.
[37]
Zezhi Shao, Zhao Zhang, FeiWang, 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 and Data Mining. 1567--1577.
[38]
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. 2022. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022).
[39]
Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems 30 (2017).
[40]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[41]
Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2018. Crosscity transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386 (2018).
[42]
Ying Wei, Yu Zheng, and Qiang Yang. 2016. Transfer knowledge between cities. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1905--1914.
[43]
Billy M Williams and Lester A Hoel. 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of transportation engineering 129, 6 (2003), 664--672.
[44]
ZonghanWu, 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.
[45]
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019).
[46]
Huaxiu Yao, Yiding Liu, YingWei, 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.
[47]
Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2018. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. arXiv:1803.01254 [cs.LG]
[48]
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017).
[49]
Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, and Jun Luo. 2022. STrans- GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation. In 2022 IEEE International Conference on Data Mining. IEEE, 743--752.
[50]
Chuanpan Zheng, Xiaoliang Fan, ChengWang, 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.
[51]
Zheng Zhu, Bo Peng, Chenfeng Xiong, and Lei Zhang. 2016. Short-term traffic flow prediction with linear conditional Gaussian Bayesian network. Journal of Advanced Transportation 50, 6 (2016), 1111--1123.
Index Terms
- Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Recommendations
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningSpatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to ...
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 ...
Long-term forecasting of Internet backbone traffic
We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute ...
Comments
Information & Contributors
Information
Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
Copyright © 2023 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 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: 21 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Provincial Key Research and Development Program of Zhejiang
Conference
CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
Acceptance Rates
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 301Total Downloads
- Downloads (Last 12 months)301
- Downloads (Last 6 weeks)21
Reflects downloads up to 27 Jul 2024
Other Metrics
Citations
Cited By
View all- Liu YWu XTang YLi XSun DZheng L(2024)Decomposition with feature attention and graph convolution network for traffic forecastingKnowledge-Based Systems10.1016/j.knosys.2024.112193300(112193)Online publication date: Oct-2024
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.
Sign in