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http://hdl.handle.net/10397/95976
Title: | Domain Adversarial Spatial-Temporal Network : a transferable framework for short-term traffic forecasting across cities | Authors: | Tang, Y Qu, A Chow, AHF Lam, WHK Wong, SC Ma, W |
Issue Date: | 17-Oct-2022 | Source: | In CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, p. 1905-1915. New York, NY, USA: Association for Computing Machinery, 2022 | Abstract: | Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the “forecasting-related knowledge” across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DastNet). DastNet is pre-trained on multiple source networks and fine-tuned with the target network’s traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DastNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DastNet is applied to Hong Kong’s new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data. Source codes of DastNet are available at https://github.com/YihongT/DASTNet. | Keywords: | Traffic forecasting Transfer learning Domain adaptation Adversarial learning Intelligent transportation systems |
Publisher: | Association for Computing Machinery | ISBN: | 978-1-4503-9236-5 | DOI: | 10.1145/3511808.3557294 | Description: | CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, Atlanta, GA, USA, October 17 - 21, 2022 | Rights: | © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, http://dx.doi.org/10.1145/3511808.3557294. The following publication 2022. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, NY, USA is available at https://dl.acm.org/doi/abs/10.1145/3511808.3557294. |
Appears in Collections: | Conference Paper |
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Tang_Transferable_Traffic_Forecasting.pdf | Pre-Published version | 5.38 MB | Adobe PDF | View/Open |
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