{"id":"W4313855663","doi":"10.1109/tits.2022.3233801","title":"A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Convolutional neural network; Traffic congestion; Floating car data; Traffic flow (computer networking); Intelligent transportation system; Traffic congestion reconstruction with Kerner's three-phase theory; Advanced Traffic Management System; Real-time computing; Deep learning; Artificial intelligence; Transport engineering; Engineering; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036797,0.0002871929,0.0002873815,0.000945631,0.0001131074,0.00005608334,0.0001289523,0.0002278795,0.00001590487],"category_scores_gemma":[0.000001291402,0.0003095232,0.0001894641,0.0008746609,0.00003517109,0.0003218709,7.534367e-8,0.0002443977,0.00004557375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711399,"about_ca_system_score_gemma":0.00001593636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002075227,"about_ca_topic_score_gemma":0.00008440822,"domain_scores_codex":[0.9981402,0.00003898303,0.0007534479,0.0003849509,0.000333413,0.0003490382],"domain_scores_gemma":[0.9995096,0.00006718937,0.00004763485,0.0002059943,0.00006590584,0.0001036777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009572884,0.0001582209,0.00002940247,0.0004026709,0.00006803274,0.000002197793,0.001244223,0.9748592,0.0006085471,0.000239557,0.005275834,0.01701634],"study_design_scores_gemma":[0.0009900623,0.000261109,0.00153219,0.0002139961,0.00008702622,0.000003015662,0.001202306,0.9833089,0.004058633,0.00000843662,0.00798509,0.0003492685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03425027,0.0000613114,0.9540214,0.00002876878,0.001686753,0.002114036,0.0003970908,0.006919123,0.0005212572],"genre_scores_gemma":[0.9960684,0.0004233584,0.0003032042,0.00001388835,0.00006436987,0.002178701,0.0004973019,0.0000755052,0.0003753354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9618181,"threshold_uncertainty_score":0.9999357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02046050473249085,"score_gpt":0.2448319697038101,"score_spread":0.2243714649713192,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}