{"id":"W2161843229","doi":"10.1002/atr.1256","title":"Optimization of pedestrian phase patterns at signalized intersections: a multi‐objective approach","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pedestrian; Intersection (aeronautics); Heuristic; Genetic algorithm; Sensitivity (control systems); Computer science; Signal timing; Phase (matter); Simulation; Mathematical optimization; Transport engineering; Engineering; Control (management); Artificial intelligence; Machine learning; Mathematics; Electronic engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003069294,0.0001159589,0.0002466933,0.0002444679,0.0001898311,0.00002449612,0.0001119943,0.00009393737,0.0002655757],"category_scores_gemma":[0.00006988216,0.0001130113,0.0001427702,0.0003547702,0.00007920209,0.001052054,8.363735e-7,0.0001319833,0.000001717732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140293,"about_ca_system_score_gemma":0.0001254016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000791195,"about_ca_topic_score_gemma":0.001201067,"domain_scores_codex":[0.9984546,0.0001149153,0.0007350415,0.0001380696,0.0003897837,0.0001676379],"domain_scores_gemma":[0.9979917,0.00008306414,0.0009248512,0.00007052912,0.0008098565,0.0001200032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000421544,0.0003817371,0.01328461,0.00004527922,0.00005882638,0.000003097077,0.04027518,0.9415498,0.001718753,0.00006244109,0.00002557749,0.002173119],"study_design_scores_gemma":[0.03808331,0.00227854,0.7257461,0.0009638923,0.0009092095,0.00001987295,0.1829601,0.03840788,0.007270343,0.0007812041,0.001303221,0.001276348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5491751,0.00005991839,0.449997,0.00006590689,0.000208219,0.0002978688,0.00002332406,0.00002234474,0.0001502951],"genre_scores_gemma":[0.9511526,0.0002637799,0.04820237,0.00002049261,0.00006703247,0.00001990986,0.0001620119,0.00001468464,0.00009714087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.903142,"threshold_uncertainty_score":0.4608468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02046771538295306,"score_gpt":0.3060145829796856,"score_spread":0.2855468675967325,"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."}}