{"id":"W2130571608","doi":"10.5555/1161734.1161998","title":"Optimization of traffic signal light timing using simulation","year":2004,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Traffic signal; Computer science; Signal timing; SIGNAL (programming language); Traffic flow (computer networking); Traffic simulation; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic congestion; Real-time computing; Simulation; Network traffic simulation; Traffic optimization; Floating car data; Transport engineering; Engineering; Network traffic control; Microsimulation; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005829108,0.0005773946,0.0005910419,0.0006530713,0.0003324341,0.0006035434,0.0005063596,0.0004787876,0.001488953],"category_scores_gemma":[0.001437382,0.000370104,0.0005785956,0.0006047566,0.0003802816,0.0004588097,0.0004886695,0.0003810266,0.0001915231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008698787,"about_ca_system_score_gemma":0.001189849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00607137,"about_ca_topic_score_gemma":0.004434508,"domain_scores_codex":[0.9995466,0.0002141959,0.00001674752,0.00004172288,0.0001012082,0.00007944569],"domain_scores_gemma":[0.999443,0.0002991044,0.00006513752,0.00005258302,0.00009911488,0.00004094936],"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":[0.00002033525,0.00001153979,0.0003654442,0.000009249394,0.000006515859,0.000009156591,0.000007930417,0.9940075,0.001005518,0.001095826,0.00008411247,0.003376854],"study_design_scores_gemma":[0.000005758473,0.00001514308,0.00006931052,0.00000175077,0.000004716223,0.000003456624,0.000004060527,0.9981365,0.0008864761,0.0004419224,0.0004282845,0.000002667478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2246458,0.000197209,0.7522436,0.0001593745,0.00003786524,0.0001892886,0.0001833567,0.001392274,0.02095122],"genre_scores_gemma":[0.9246129,0.0001495301,0.07299785,0.00001944985,0.000007126667,0.0001387422,0.0001708424,0.00008734644,0.001816219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00607137,"threshold_uncertainty_score":0.01207203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06767652236324061,"score_gpt":0.3377629388672429,"score_spread":0.2700864165040023,"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."}}