{"id":"W4281788403","doi":"10.1155/2022/7096153","title":"A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China; Department of Transportation of Hunan Province; U.S. Department of Transportation","keywords":"Crowding; Computer science; Transport engineering; Engineering; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005501712,0.0004926447,0.0004461918,0.0004155833,0.0003720373,0.00042013,0.00115611,0.0005859913,0.001661773],"category_scores_gemma":[0.001318208,0.0002559591,0.000612287,0.0005816818,0.000227659,0.0007340848,0.0007155249,0.0008333157,0.0004481693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008039446,"about_ca_system_score_gemma":0.001131056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05479446,"about_ca_topic_score_gemma":0.04037103,"domain_scores_codex":[0.9997815,0.00003748333,0.00001165601,0.00009834508,0.00003135372,0.000039551],"domain_scores_gemma":[0.9997104,0.0001179446,0.0000459065,0.00002590005,0.00007447596,0.00002533344],"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":[0.00005092973,0.00004598411,0.005211189,0.00002632152,0.00002461918,0.0000853028,0.00005943725,0.9554628,0.0009782015,0.004757302,0.001584813,0.03171312],"study_design_scores_gemma":[0.000001430717,0.000005540024,0.0004194275,0.000001128355,0.000003398617,0.00000456539,0.000003593957,0.9986926,0.00004920405,0.0007042914,0.0001122894,0.000002533555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.225156,0.0002965762,0.765379,0.0008600524,0.00008830535,0.0001168262,0.002510354,0.001531082,0.004061735],"genre_scores_gemma":[0.9467217,0.0002135355,0.04656803,0.00007733595,0.00004776258,0.0001449667,0.001537369,0.00003890098,0.004650408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05479446,"threshold_uncertainty_score":0.108951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339063462436305,"score_gpt":0.3003199694344114,"score_spread":0.2869293348100483,"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."}}