{"url":"/dataset/tte-a-o","name":"TTE-A&O","full_name":"Travel Time Estimation: Abakan and Omsk","description_markdown":"The dataset includes two parts corresponding to the cities of Abakan (65524 nodes, 340012 edges) and Omsk (231688 nodes, 1149492 edges). Along with the road network graph, it includes trip records represented as sequences of visited nodes (making the dataset suitable both for path-blind and path-aware settings). There are two types of target values for a regression task: real travel time and real length of a trip.","description_withheld":null,"homepage":"https://github.com/Eighonet/GCT-TTE","introduced_date":"2023-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/gct-tte-graph-convolutional-transformer-for","title":"GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation","first_author":"Vladimir Mashurov","url":null},"license":{"name":"MIT","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"regression","url":"/task/regression-1","datasets_with_task":"/datasets/task/regression-1"},{"name":"Travel Time Estimation","url":"/task/travel-time-estimation","datasets_with_task":"/datasets/task/travel-time-estimation"}],"languages":[],"variants":["TTE-A&O"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset_variant":"TTE-A&O","rows":6,"metrics":["Root mean square error (RMSE)","mean absolute error"],"first_row_in_archive_order":{"model":"GCT-TTE","paper":"/paper/gct-tte-graph-convolutional-transformer-for","metrics":{"Root mean square error (RMSE)":"147.89","mean absolute error":"92.26"},"code_links":[{"title":"eighonet/gct-tte","url":"https://github.com/eighonet/gct-tte"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gct-tte-graph-convolutional-transformer-for","title":"GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation","date":"2023-06-07","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/logistics-graphs-and-transformers-towards","title":"Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation","date":"2022-07-12","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}