{"url":"/dataset/pemsd8","name":"PeMSD8","full_name":null,"description_markdown":"This dataset contains the traffic data in San Bernardino from July to August in 2016, with 170 detectors on 8 roads with a time interval of 5 minutes. This dataset is popular as a benchmark traffic forecasting dataset.","description_withheld":null,"homepage":"https://github.com/wanhuaiyu/ASTGCN#datasets","introduced_date":"2019-01-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/attention-based-spatial-temporal-graph","title":"Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting","first_author":"Shengnan Guo","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"Multivariate Time Series Forecasting","url":"/task/multivariate-time-series-forecasting","datasets_with_task":"/datasets/task/multivariate-time-series-forecasting"},{"name":"Traffic Prediction","url":"/task/traffic-prediction","datasets_with_task":"/datasets/task/traffic-prediction"},{"name":"Correlated Time Series Forecasting","url":"/task/correlated-time-series-forecasting","datasets_with_task":"/datasets/task/correlated-time-series-forecasting"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["PeMSD8"],"data_loaders":[{"repo":"https://github.com/wanhuaiyu/ASTGCN","url":"https://github.com/wanhuaiyu/ASTGCN","frameworks":[]}],"num_papers_in_archive":44,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-prediction-on-pemsd8","task":"Traffic Prediction","dataset_variant":"PeMSD8","rows":13,"metrics":["12 steps MAE","12 steps MAPE","12 steps RMSE","MAE@1h"],"first_row_in_archive_order":{"model":"Hierarchical-Attention-LSTM (HierAttnLSTM)","paper":"/paper/big-data-application-for-network-level-travel","metrics":{"12 steps MAE":"9.215","12 steps RMSE":"22.320"},"code_links":[{"title":"TeRyZh/Network-Level-Travel-Prediction-Hierarchical-Attention-LSTM","url":"https://github.com/TeRyZh/Network-Level-Travel-Prediction-Hierarchical-Attention-LSTM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fastersts-a-faster-spatio-temporal","title":"FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting","date":"2025-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pdg2seq-periodic-dynamic-graph-to-sequence","title":"PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction","date":"2024-12-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pattern-matching-dynamic-memory-network-for-1","title":"Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction","date":"2024-08-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/a-novel-hybrid-time-varying-graph-neural","title":"A novel hybrid time-varying graph neural network for traffic flow forecasting","date":"2024-01-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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":"2026-09-25T09:33:49+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/when-spatio-temporal-meet-wavelets","title":"When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks","date":"2023-07-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-decomposition-dynamic-graph-convolutional","title":"A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting","date":"2023-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-neural-rough-differential-equations-for","title":"Graph Neural Rough Differential Equations for Traffic Forecasting","date":"2023-03-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":17,"samples_ran":13,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pdformer-propagation-delay-aware-dynamic-long","title":"PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction","date":"2023-01-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hagcn-network-decentralization-attention","title":"HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting","date":"2022-09-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/big-data-application-for-network-level-travel","title":"Network Level Spatial Temporal Traffic State Forecasting with Hierarchical Attention LSTM (HierAttnLSTM)","date":"2022-01-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-neural-controlled-differential","title":"Graph Neural Controlled Differential Equations for Traffic Forecasting","date":"2021-12-07","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":3,"samples_harvested":28,"samples_ran":21,"samples_unverified":7,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":0,"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."}