{"id":"W4238787331","doi":"10.5383/jttm.02.02.001","title":"Model calibration to simulate driving recommendations for traffic flow optimization in oversaturated city traffic","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Queue; Traffic flow (computer networking); Calibration; Traffic signal; Computer science; Traffic wave; Traffic conflict; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic bottleneck; Traffic simulation; Traffic optimization; Queueing theory; Simulation; Transport engineering; Real-time computing; Traffic congestion; Floating car data; Microsimulation; Engineering; Computer network; Statistics; Mathematics","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":[0.0001667353,0.0001698122,0.0002105221,0.0003252278,0.0000423664,0.00008362433,0.0001825547,0.00004897074,0.00002750648],"category_scores_gemma":[0.00001055866,0.0001803437,0.00009887572,0.0002154333,0.00001078827,0.0004056352,0.000005884913,0.0001174005,8.362354e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008512724,"about_ca_system_score_gemma":0.00001584375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.303039e-7,"about_ca_topic_score_gemma":0.00006176838,"domain_scores_codex":[0.9986841,0.0000150311,0.0006657831,0.0001895509,0.0002848168,0.0001607378],"domain_scores_gemma":[0.9995334,0.00003996753,0.0001196955,0.00005387078,0.000123521,0.0001295942],"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":[0.0001588938,0.00005883042,0.0000116858,0.00005021556,0.00019477,0.00001503944,0.001685869,0.9306789,0.00001645816,0.0002165379,0.0009122793,0.0660005],"study_design_scores_gemma":[0.002235584,0.00007474351,0.0008394035,0.00007690588,0.0000886972,0.000001043588,0.0003917281,0.9940484,0.00000491428,0.00001188392,0.002058231,0.0001684645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3535386,0.00006244232,0.6387232,0.006410193,0.0004865543,0.0005691559,0.00005489731,0.0001137292,0.00004119563],"genre_scores_gemma":[0.9652883,0.0002707218,0.03374105,0.0003846133,0.00009015144,0.00002577627,0.0001532068,0.00002383508,0.00002232051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6117497,"threshold_uncertainty_score":0.7354201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725402000011768,"score_gpt":0.2393588010339445,"score_spread":0.2221047810338268,"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."}}