{"id":"W2587886417","doi":"10.1155/2017/6905431","title":"Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Mean squared error; Computer science; Simulation; Mean squared prediction error; Guidance system; Travel time; Penetration rate; Calibration; Engineering; Statistics; Machine learning; Mathematics; Transport 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":[0.0001024994,0.000111616,0.0001524411,0.000109774,0.0001027026,0.00003675673,0.0001602336,0.00005645335,0.000003227711],"category_scores_gemma":[0.00003332889,0.0001142971,0.00006830029,0.00004067297,0.00002012019,0.0005665053,0.000001055464,0.000178997,0.000001210547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000825438,"about_ca_system_score_gemma":0.00001596415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001053749,"about_ca_topic_score_gemma":0.00001874428,"domain_scores_codex":[0.9992034,0.000006724259,0.0003859849,0.00008749213,0.0002067434,0.0001095916],"domain_scores_gemma":[0.9993217,0.00001819013,0.0002912223,0.0001996232,0.0001139479,0.00005530368],"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":[0.00007189291,0.00004015954,0.001436838,0.00005269514,0.00002387618,0.000007339829,0.00008297983,0.9662881,0.01042169,0.0001230089,0.000292102,0.02115929],"study_design_scores_gemma":[0.0006720998,0.00008012179,0.3675524,0.0001139505,0.00003120277,5.090563e-7,0.000008408325,0.629948,0.0007745597,0.00004526209,0.0007047513,0.00006873684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.460448,0.00002216275,0.5376766,0.0001339257,0.0006431086,0.0001281281,0.00007896109,0.000548358,0.0003207051],"genre_scores_gemma":[0.9908084,0.0001464719,0.008847579,0.00005383028,0.00004965237,0.000007591165,0.00004317119,0.00002264343,0.00002063157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5303605,"threshold_uncertainty_score":0.4660902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007098383601862878,"score_gpt":0.2378129102399237,"score_spread":0.2307145266380608,"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."}}