{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatio-temporal-meta-graph-learning-for","title":"Spatio-Temporal Meta-Graph Learning for Traffic Forecasting","arxiv_id":"2211.14701","date":"2022-11-27","proceeding":null,"authors":["Renhe Jiang","Zhaonan Wang","Jiawei Yong","Puneet Jeph","Quanjun Chen","Yasumasa Kobayashi","Xuan Song","Shintaro Fukushima","Toyotaro Suzumura"],"abstract":"Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.","url_abs":"https://arxiv.org/abs/2211.14701v4","url_pdf":"https://arxiv.org/pdf/2211.14701v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatio-temporal-meta-graph-learning-for","repo_url":"https://github.com/deepkashiwa20/megacrn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"graph-structure-learning","task_name":"Graph structure learning"},{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"expy-tky","name":"EXPY-TKY","full_name":"Expressway-Tokyo"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/traffic-prediction-on-expy-tky-1","task":"Traffic Prediction","dataset":"EXPY-TKY","model":"MegaCRN","rank_in_archive_order":2,"of":8,"metrics":{"1 step MAE":"5.81","3 step MAE":"6.44","6 step MAE":"6.83"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-metr-la","task":"Traffic Prediction","dataset":"METR-LA","model":"MegaCRN","rank_in_archive_order":9,"of":20,"metrics":{"MAE @ 12 step":"3.38","MAE @ 3 step":"2.63"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems-bay","task":"Traffic Prediction","dataset":"PEMS-BAY","model":"MegaCRN","rank_in_archive_order":8,"of":16,"metrics":{"MAE @ 12 step":"1.88","RMSE ":"4.42"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.14701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14701"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/deepkashiwa20/megacrn","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"fde1716c2510a3a6","entry":"generate_graph_seq2seq_io_data","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"fde1716c2510a3a6"}},{"code_sha256_prefix":"8f6da201324530dd","entry":"prepare_x_y","repo":"deepkashiwa20/megacrn","repo_kind":"official","path":"model/traintest_MegaCRN.py","file_url":"https://github.com/deepkashiwa20/megacrn/blob/HEAD/model/traintest_MegaCRN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8f6da201324530dd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}