{"id":"W2377029833","doi":"10.1139/cjce-2016-0050","title":"A micro-simulation study on proactive coordinated ramp metering for relieving freeway congestion","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic control and management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Shandong University","keywords":"Metering mode; Traffic flow (computer networking); Traffic congestion; Computer science; Control (management); Travel time; Reduction (mathematics); Traffic simulation; Field (mathematics); Simulation; Transport engineering; Real-time computing; Engineering; Microsimulation; Computer network; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728319,0.0001611937,0.0002205847,0.0004157891,0.00004676657,0.0000446452,0.0001245588,0.00004776028,0.00001583615],"category_scores_gemma":[0.0001871598,0.0001341859,0.00007842919,0.0001176715,0.000007679905,0.0001919114,0.0000050206,0.0001158044,0.000002384076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003675531,"about_ca_system_score_gemma":0.00005502615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004331618,"about_ca_topic_score_gemma":0.005673151,"domain_scores_codex":[0.9991855,0.0000119761,0.0003075882,0.000107596,0.00009559734,0.0002917469],"domain_scores_gemma":[0.9993162,0.0001632719,0.00006352401,0.0001154522,0.0001096616,0.0002318989],"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":[0.0000227359,0.00001244263,0.0001259438,0.00003330669,0.0002155207,0.00002783782,0.0003397813,0.97456,0.01191977,0.00006248198,0.0002206628,0.01245958],"study_design_scores_gemma":[0.02156088,0.004004367,0.1598013,0.003573028,0.0009411653,0.0001184577,0.001343368,0.7269125,0.008504964,0.0002286461,0.07028963,0.002721756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5496985,0.0003607058,0.4465379,0.000215042,0.001679742,0.0009209275,0.00002160447,0.000160994,0.000404646],"genre_scores_gemma":[0.9994561,0.000005016461,0.0002663973,0.00000844198,0.0001603517,0.00002277219,7.526494e-7,0.00004406017,0.00003609158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4497577,"threshold_uncertainty_score":0.5471943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180979624457058,"score_gpt":0.1989170232008099,"score_spread":0.1871072269562393,"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."}}