{"url":"/dataset/expy-tky","name":"EXPY-TKY","full_name":"Expressway-Tokyo","description_markdown":"EXPY-TKY contains the traffic speed information and the corresponding traffic incident information in 10-minute interval for 1843 expressway road links in Tokyo over three months (2021/10∼2021/12). Compared with other benchmarks for traffic prediction, EXPY-TKY covers a larger scale and more complex incident situations. Potential tasks of EXPY-TKY include traffic prediction, incident detection, and road type classification.","description_withheld":null,"homepage":"https://github.com/deepkashiwa20/MegaCRN","introduced_date":"2022-11-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/spatio-temporal-meta-graph-learning-for","title":"Spatio-Temporal Meta-Graph Learning for Traffic Forecasting","first_author":"Renhe Jiang","url":null},"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Traffic Prediction","url":"/task/traffic-prediction","datasets_with_task":"/datasets/task/traffic-prediction"}],"languages":[],"variants":["EXPY-TKY"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-prediction-on-expy-tky-1","task":"Traffic Prediction","dataset_variant":"EXPY-TKY","rows":8,"metrics":["1 step MAE","3 step MAE","6 step MAE"],"first_row_in_archive_order":{"model":"STD-MAE","paper":"/paper/spatio-temporal-decoupled-masked-pre-training","metrics":{"1 step MAE":"5.73","3 step MAE":"6.41","6 step MAE":"6.75"},"code_links":[{"title":"jimmy-7664/std-mae","url":"https://github.com/jimmy-7664/std-mae"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/spatio-temporal-decoupled-masked-pre-training","title":"Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting","date":"2023-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spatio-temporal-meta-graph-learning-for","title":"Spatio-Temporal Meta-Graph Learning for Traffic Forecasting","date":"2022-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-to-remember-patterns-pattern-1","title":"Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting","date":"2021-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":8,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spectral-temporal-graph-neural-network-for-1","title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting","date":"2021-03-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":2,"samples_unverified":11,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adaptive-graph-convolutional-recurrent","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","date":"2020-07-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/connecting-the-dots-multivariate-time-series","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","date":"2020-05-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/190600121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","date":"2019-05-31","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/diffusion-convolutional-recurrent-neural","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","date":"2017-07-06","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":35,"samples_ran":17,"samples_unverified":18,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":79,"samples_ran":40,"samples_unverified":39,"pointer_only_for_licence":24,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}