{"url":"/dataset/pems04","name":"PeMS04","full_name":null,"description_markdown":"PeMS04 is a traffic forecasting benchmark.","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":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":"Fine-Grained Urban Flow Inference","url":"/task/fine-grained-urban-flow-inference","datasets_with_task":"/datasets/task/fine-grained-urban-flow-inference"},{"name":"Correlated Time Series Forecasting","url":"/task/correlated-time-series-forecasting","datasets_with_task":"/datasets/task/correlated-time-series-forecasting"}],"languages":[],"variants":["PeMS04","PEMS-BAY","TaxiBJ-P1"],"data_loaders":[],"num_papers_in_archive":37,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-prediction-on-pems04","task":"Traffic Prediction","dataset_variant":"PeMS04","rows":12,"metrics":["12 Steps MAE","FLOPs(M)","MAE","MAPE","Parameters(K)","RMSE"],"first_row_in_archive_order":{"model":"STD-MAE","paper":"/paper/spatio-temporal-decoupled-masked-pre-training","metrics":{"12 Steps MAE":"17.80"},"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"},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset_variant":"TaxiBJ-P1","rows":9,"metrics":["MSE","MAE","MAPE"],"first_row_in_archive_order":{"model":"STCF","paper":"/paper/spatial-temporal-contrasting-for-fine-grained","metrics":{"MSE":"14.9232"},"code_links":[{"title":"Xovee/stcf","url":"https://github.com/Xovee/stcf"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dynamic-trend-fusion-module-for-traffic-flow","title":"Dynamic Trend Fusion Module for Traffic Flow Prediction","date":"2025-01-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/a-gated-mlp-architecture-for-learning","title":"Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks","date":"2024-01-29","rows_on_this_dataset":1,"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":"2026-09-24T18:15:14+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/spatial-temporal-contrasting-for-fine-grained","title":"Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference","date":"2023-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spatio-temporal-adaptive-embedding-makes","title":"STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting","date":"2023-08-21","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/lightcts-a-lightweight-framework-for","title":"LightCTS: A Lightweight Framework for Correlated Time Series Forecasting","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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