{"id":"W3090267487","doi":"10.1155/2020/1462430","title":"An Innovative Eigenvector-Based Method for Traffic Light Scheduling","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Traffic signal; Computer science; Intersection (aeronautics); Scheduling (production processes); Grid; Relation (database); Subnetwork; Cell Transmission Model; Traffic wave; Traffic congestion reconstruction with Kerner's three-phase theory; Real-time computing; Computer network; Transport engineering; Mathematical optimization; Engineering; Mathematics; Data mining; Traffic congestion","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":[0.0001289165,0.000108318,0.0002035042,0.00008776944,0.00002680022,0.00001267863,0.00008785626,0.00003335596,0.000007991084],"category_scores_gemma":[0.00001290706,0.0001025013,0.00008612087,0.0002580052,0.000004426764,0.0002999939,2.459192e-7,0.0001193988,5.638489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002663774,"about_ca_system_score_gemma":0.00002660579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.933643e-7,"about_ca_topic_score_gemma":0.000009655642,"domain_scores_codex":[0.9992383,0.00001113717,0.0004038163,0.0000920038,0.0001350989,0.0001196883],"domain_scores_gemma":[0.9995071,0.00003808434,0.0001282718,0.00004978808,0.0001910724,0.00008569624],"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":[0.0001852622,0.00002390408,0.000008395238,0.00009298683,0.00005616681,0.000006585961,0.001583298,0.8894856,0.04642539,0.0001577051,0.00001801879,0.06195669],"study_design_scores_gemma":[0.01048919,0.00208333,0.02760465,0.0001652481,0.0003454092,0.000002317168,0.00240634,0.8928571,0.03076048,0.0001647751,0.03247692,0.0006441855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3398834,0.0001453591,0.6589611,0.0005281725,0.0002160634,0.0001691446,0.00000911746,0.00007726147,0.00001042968],"genre_scores_gemma":[0.7903981,0.00001122612,0.2092534,0.0001428877,0.0001467783,0.000008497246,0.00001869596,0.0000196181,7.512654e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4505147,"threshold_uncertainty_score":0.4179883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01116491687853093,"score_gpt":0.2586094207826027,"score_spread":0.2474445039040717,"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."}}