{"id":"W3163630458","doi":"10.1155/2021/6645214","title":"A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Computer science; Benchmark (surveying); Traffic congestion; Real-time computing; Term (time); Urban rail transit; Deep learning; Transport engineering; Simulation; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223409,0.0001066816,0.0001543749,0.0000871306,0.00005018679,0.00001855879,0.00003888526,0.0000686836,0.000003570316],"category_scores_gemma":[0.000009195761,0.0001021272,0.00007030663,0.0001261637,0.00001235393,0.0004554204,6.47082e-7,0.0002177272,1.812021e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005144803,"about_ca_system_score_gemma":0.00001792911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.124201e-7,"about_ca_topic_score_gemma":0.00003334172,"domain_scores_codex":[0.9992715,0.00001236895,0.000342039,0.00009475786,0.0001541539,0.0001252328],"domain_scores_gemma":[0.9994459,0.00003116429,0.0001351799,0.00005669539,0.0002762607,0.00005475547],"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":[0.0001005979,0.00002118853,0.0002509189,0.00005055277,0.00007088608,0.00001576736,0.0002760853,0.973568,0.001940128,0.0003418917,0.0002027786,0.02316126],"study_design_scores_gemma":[0.001345381,0.0002177083,0.01090938,0.0001207593,0.000154918,0.00002303423,0.0003151316,0.9827939,0.00123299,0.0001077477,0.002657762,0.0001212475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06210416,0.0003453678,0.9365035,0.00005023922,0.0002764641,0.0001515001,0.000004895959,0.0004780572,0.00008580122],"genre_scores_gemma":[0.9603911,0.0009759981,0.03833742,0.00002410428,0.00008958221,0.00002421149,0.00009929637,0.00002840715,0.00002986693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8982869,"threshold_uncertainty_score":0.4164628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005445973574697019,"score_gpt":0.2046552011934301,"score_spread":0.1992092276187331,"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."}}