{"id":"W4306409415","doi":"10.1049/itr2.12298","title":"Hybrid models of subway passenger flow prediction based on convolutional neural network","year":2022,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Artificial neural network; Convolution (computer science); Flow (mathematics); Line (geometry); Term (time); Recurrent neural network; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003657084,0.0007389051,0.0005358405,0.0005521468,0.0002740604,0.000683762,0.0009863785,0.0006646661,0.001929232],"category_scores_gemma":[0.0005794052,0.0003804412,0.0007041841,0.0004727375,0.0003004092,0.0008215663,0.0005493069,0.0006251027,0.0003004639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215118,"about_ca_system_score_gemma":0.001005687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06206108,"about_ca_topic_score_gemma":0.03706031,"domain_scores_codex":[0.9998361,0.0000183711,0.000009565459,0.00006377693,0.00003037196,0.00004180013],"domain_scores_gemma":[0.999805,0.00006384317,0.00002832526,0.00001570521,0.0000710305,0.00001605347],"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":[0.00006959643,0.00003119629,0.001661589,0.00002005532,0.00004019429,0.00003816462,0.00001959143,0.9768606,0.001283522,0.0009334856,0.0003752035,0.01866673],"study_design_scores_gemma":[6.653471e-7,0.000003070068,0.0001359376,7.650682e-7,0.00000222997,0.000001313877,7.42637e-7,0.9996156,0.00009828853,0.0001070432,0.00003322137,0.000001132289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4458983,0.001503762,0.5373198,0.0005593497,0.0002201289,0.00007112303,0.0008710757,0.0021564,0.01140015],"genre_scores_gemma":[0.9867266,0.0001954732,0.008898465,0.00003015333,0.00001623352,0.00003378275,0.0002228339,0.00002032667,0.003856206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06206108,"threshold_uncertainty_score":0.1233997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658995682327004,"score_gpt":0.1919588961325088,"score_spread":0.1753689393092388,"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."}}