{"id":"W3009324813","doi":"10.1109/icsai48974.2019.9010150","title":"Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters","year":2019,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Cluster analysis; Traffic congestion; Floating car data; Intelligent transportation system; Traffic flow (computer networking); Traffic congestion reconstruction with Kerner's three-phase theory; Focus (optics); Similarity (geometry); Function (biology); Internet of Things; Computer network; The Internet; Data mining; Real-time computing; Transport engineering; Artificial intelligence; Engineering; Computer security; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.00005971564,0.00009885384,0.00008429971,0.0001268284,0.00002837527,0.00002944489,0.00007214198,0.00005477395,0.00007314525],"category_scores_gemma":[0.000003136605,0.00009642145,0.00003694894,0.00008668011,0.000008171271,0.0001279001,0.00001373987,0.00009102532,0.00009251171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007219019,"about_ca_system_score_gemma":0.000002855225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002777592,"about_ca_topic_score_gemma":0.000005530274,"domain_scores_codex":[0.9995037,0.000008160202,0.0001087882,0.000118765,0.0001073361,0.0001532721],"domain_scores_gemma":[0.9997817,0.00001367543,0.00001047697,0.0001476431,0.000008720258,0.00003780119],"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":[0.000007548614,0.00001247091,0.00007044835,0.00004498019,0.00002394171,0.000002168648,0.0001162394,0.9385809,0.0007586983,0.001321081,0.03242134,0.02664023],"study_design_scores_gemma":[0.0002156311,0.00003740263,0.0002844765,0.00003956738,0.000007909678,0.00000194043,0.00006081828,0.9913221,0.0003975512,0.0000051164,0.007512478,0.0001149891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7530196,0.00003492359,0.1604819,0.00008379498,0.001144858,0.000508073,0.000002620547,0.0125058,0.07221838],"genre_scores_gemma":[0.9967779,0.00001304842,0.002635918,0.00009845506,0.00004826678,0.000006415041,0.00000675051,0.0000235867,0.0003896751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2437583,"threshold_uncertainty_score":0.3931953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009387363887729748,"score_gpt":0.2026110200183986,"score_spread":0.1932236561306689,"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."}}