{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002394629,0.0001078821,0.0001419638,0.000159333,0.0002560025,0.0000679,0.0001130322,0.0001149309,0.0003848296],"category_scores_gemma":[0.00008807815,0.0001209671,0.00005490188,0.0003308215,0.00007772785,0.000558038,0.000005937249,0.00007396144,0.000006453216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008872253,"about_ca_system_score_gemma":0.0002109221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008702101,"about_ca_topic_score_gemma":0.00008857537,"domain_scores_codex":[0.9988356,0.00008262505,0.0003736315,0.0002021586,0.000338875,0.0001671741],"domain_scores_gemma":[0.9990079,0.0001180269,0.0002370832,0.0001006283,0.000469865,0.00006649637],"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":[0.00002659299,0.00003430376,0.0004351476,0.000008803449,0.000008160081,6.301257e-7,0.0124099,0.9846842,0.0001003479,0.001174989,0.000001014771,0.001115913],"study_design_scores_gemma":[0.0004444205,0.00002651077,0.0003890094,0.0001065915,0.00002457794,1.359203e-7,0.0007771301,0.9975899,0.0001764588,0.0001275982,0.0002041004,0.0001334973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.16744,0.00001122724,0.8307393,0.00017155,0.0001430716,0.0001813377,0.000004947944,0.00009703948,0.001211529],"genre_scores_gemma":[0.9827403,0.000003769211,0.01698981,0.0000375854,0.00008516823,0.000002010909,0.00004698241,0.00001199211,0.00008241168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8153003,"threshold_uncertainty_score":0.4932896,"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."}}