{"id":"W4382196352","doi":"10.1155/2023/3463330","title":"A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"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; Markov chain; Mobility model; Benchmark (surveying); Computer science; Markov model; Markov process; Ordinary least squares; Flow (mathematics); Econometrics; Machine learning; Statistics; Mathematics; Distributed computing; Geography","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":[0.0009391551,0.00007225104,0.0001555653,0.0002109094,0.0004226091,0.00002602234,0.00009716114,0.00005850007,0.00007578287],"category_scores_gemma":[0.00006988437,0.00007618181,0.0000804955,0.0006409131,0.00003073692,0.0005646296,0.000002757754,0.0001257009,0.00000547289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007728425,"about_ca_system_score_gemma":0.000241955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000190126,"about_ca_topic_score_gemma":0.002446734,"domain_scores_codex":[0.998826,0.00005221825,0.0003883695,0.0001489169,0.0004059033,0.0001785992],"domain_scores_gemma":[0.999311,0.00008148498,0.0001754582,0.00007554296,0.0001554449,0.0002010432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001679759,0.0002534033,0.03603676,0.00006105487,0.000118613,0.000007327813,0.05315092,0.8823555,0.002259974,0.004669156,0.001527019,0.01939236],"study_design_scores_gemma":[0.001631014,0.0002499736,0.9272687,0.0001389202,0.0003058999,0.00000113332,0.01975618,0.005861389,0.0001505509,0.03813237,0.006265741,0.0002380813],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8819372,0.00003029461,0.1168528,0.0006630459,0.00008304902,0.0002196135,0.0001071335,0.00004198465,0.00006484536],"genre_scores_gemma":[0.9966111,0.0000835121,0.00277345,0.00006835641,0.0001131596,0.00001526249,0.00009092059,0.000006815347,0.000237491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.891232,"threshold_uncertainty_score":0.325041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005788403660696,"score_gpt":0.3249229889338169,"score_spread":0.3048651048972099,"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."}}