{"id":"W4291178899","doi":"10.1177/03611981221112673","title":"Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model","year":2022,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Term (time); Scalability; Intelligent transportation system; Graph; Flow network; Deep learning; Convolutional neural network; Artificial neural network; Network model; Popularity; Artificial intelligence; Machine learning; Data mining; Distributed computing; Database; Theoretical computer science; Engineering; Transport engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002046743,0.000590238,0.0002661676,0.0005504421,0.0002045252,0.0003830185,0.0007175284,0.0005543753,0.001732974],"category_scores_gemma":[0.0006391503,0.0002469443,0.000405376,0.000562855,0.0002428665,0.0006967075,0.0003024733,0.0008901521,0.0003474223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120694,"about_ca_system_score_gemma":0.0008367188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07565447,"about_ca_topic_score_gemma":0.08102015,"domain_scores_codex":[0.9999261,0.000009738156,0.00000284684,0.00002525262,0.00001460371,0.00002135352],"domain_scores_gemma":[0.9998596,0.00004178668,0.00001920665,0.000009573587,0.00005729145,0.00001251879],"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":[0.00005841221,0.00004240733,0.002565821,0.00001655914,0.0000226456,0.00003636712,0.00001421533,0.9667134,0.0008988662,0.001505632,0.002079809,0.02604583],"study_design_scores_gemma":[0.000001068952,0.000003910726,0.0002524019,0.000001180962,0.000002355708,0.00000168335,0.000001573463,0.9991789,0.00012268,0.0003372696,0.00009549955,0.000001326013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4913011,0.001365869,0.4871746,0.001632518,0.0002605469,0.00008013613,0.003998418,0.003598065,0.01058874],"genre_scores_gemma":[0.9721491,0.0003240039,0.01998334,0.000111975,0.00002591543,0.00004037275,0.002047779,0.00003754378,0.005279875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07565447,"threshold_uncertainty_score":0.1504282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07234679158145943,"score_gpt":0.3261916800874278,"score_spread":0.2538448885059684,"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."}}