{"id":"W2469024231","doi":"10.1002/atr.1389","title":"A model‐based demand‐balancing control for dynamically divided multiple urban subnetworks","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Subnetwork; Traffic generation model; Network traffic control; Computer science; Traffic congestion reconstruction with Kerner's three-phase theory; Throughput; Controller (irrigation); Floating car data; Traffic congestion; Network traffic simulation; Traffic flow (computer networking); Computer network; Traffic shaping; Distributed computing; Transport engineering; Engineering; Network packet","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":[0.000425902,0.0005764252,0.0006747093,0.0003277661,0.0005812384,0.0009071745,0.0008809542,0.0004805095,0.001527668],"category_scores_gemma":[0.000537038,0.0002623431,0.0003941589,0.0003215558,0.0004584361,0.0005701934,0.0007261411,0.0005445622,0.0001652336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003816,"about_ca_system_score_gemma":0.0008976918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279376,"about_ca_topic_score_gemma":0.008185018,"domain_scores_codex":[0.999759,0.00003973831,0.00001011296,0.00006894761,0.00007240408,0.00004975909],"domain_scores_gemma":[0.9997885,0.00005041246,0.00004701679,0.00001743124,0.00007946862,0.00001728891],"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":[0.00005833504,0.0000285273,0.0002558625,0.00003038053,0.00001245165,0.00004196221,0.00002782583,0.9831792,0.003990318,0.003200523,0.0004897926,0.008684845],"study_design_scores_gemma":[0.000004894236,0.00001249222,0.00005990522,0.000001281074,0.000003426102,0.000002908252,0.000002834574,0.9991506,0.0002620241,0.0003552077,0.0001422374,0.000002102922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052981,0.000266226,0.8800288,0.0002732486,0.0001206358,0.00007222209,0.000117667,0.0009755312,0.01284748],"genre_scores_gemma":[0.9931638,0.00005176652,0.00544243,0.00001844154,0.000009887713,0.0000405824,0.00003771414,0.00001257885,0.001222824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01279376,"threshold_uncertainty_score":0.02543861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004882089186952844,"score_gpt":0.1968412958907276,"score_spread":0.1919592067037747,"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."}}