{"url":"/dataset/trancos","name":"TRANCOS","full_name":"TRaffic ANd COngestionS","description_markdown":"","description_withheld":null,"homepage":"https://gram.web.uah.es/data/datasets/trancos/index.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Object Counting","url":"/task/object-counting","datasets_with_task":"/datasets/task/object-counting"},{"name":"Crowd Counting","url":"/task/crowd-counting","datasets_with_task":"/datasets/task/crowd-counting"}],"languages":[],"variants":["TRANCOS"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/crowd-counting-on-trancos","task":"Crowd Counting","dataset_variant":"TRANCOS","rows":4,"metrics":["MAE"],"first_row_in_archive_order":{"model":"M-SFANet+M-SegNet","paper":"/paper/encoder-decoder-based-convolutional-neural","metrics":{"MAE":"2.22"},"code_links":[{"title":"Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting","url":"https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting"},{"title":"HuynhKEn/Variations-of-SFANet-for-Crowd-Counting","url":"https://github.com/HuynhKEn/Variations-of-SFANet-for-Crowd-Counting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-counting-on-trancos","task":"Object Counting","dataset_variant":"TRANCOS","rows":1,"metrics":["MAE","MSE"],"first_row_in_archive_order":{"model":"GauNet (ResNet-50)","paper":"/paper/rethinking-spatial-invariance-of-1","metrics":{"MAE":"2.1","MSE":"2.6"},"code_links":[{"title":"zhiqic/rethinking-counting","url":"https://github.com/zhiqic/rethinking-counting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-spatial-invariance-of-1","title":"Rethinking Spatial Invariance of Convolutional Networks for Object Counting","date":"2022-06-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/encoder-decoder-based-convolutional-neural","title":"Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting","date":"2020-03-12","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/from-open-set-to-closed-set-counting-objects","title":"From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer","date":"2019-08-15","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adcrowdnet-an-attention-injective-deformable","title":"ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding","date":"2018-11-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/csrnet-dilated-convolutional-neural-networks","title":"CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes","date":"2018-02-27","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":5,"samples_unverified":10,"pointer_only_for_licence":5,"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":2,"samples_harvested":23,"samples_ran":8,"samples_unverified":15,"pointer_only_for_licence":8,"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."}