{"id":"W4391054335","doi":"10.1061/jtepbs.teeng-8600","title":"Evaluating the Need for Traffic Signal Retiming Using Connected Vehicle Data","year":2024,"lang":"en","type":"preprint","venue":"Journal of Transportation Engineering Part A Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Retiming; VisSim; Signal timing; Computer science; Microsimulation; SIGNAL (programming language); Real-time computing; Metric (unit); Simulation; Traffic signal; Algorithm; Engineering; Transport engineering; Operations management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00146348,0.0003078003,0.0004774257,0.0003258849,0.0000609833,0.0001962627,0.0005774988,0.0002066132,0.000003482834],"category_scores_gemma":[0.00003640873,0.0002620461,0.0002134854,0.0002083567,0.00001529559,0.000164,0.00003183017,0.0007860874,9.003157e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001369794,"about_ca_system_score_gemma":0.00009142156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006539482,"about_ca_topic_score_gemma":0.00000437539,"domain_scores_codex":[0.997815,0.00003584808,0.001206533,0.0002458378,0.0004512648,0.0002455561],"domain_scores_gemma":[0.9988353,0.0001612713,0.0003221128,0.0004155563,0.0001861418,0.00007961716],"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":[0.00002024079,0.00001009263,0.000003558029,0.002323102,0.0004618373,0.00001145281,0.000482981,0.9876583,0.002979996,0.0001166376,0.004883504,0.001048248],"study_design_scores_gemma":[0.0003297328,0.00006657671,0.00004142825,0.00214406,0.0006653396,0.00001908995,0.0001904336,0.991905,0.0001477777,0.000009398628,0.004237374,0.0002437393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3263341,0.004783871,0.6560076,0.0001087902,0.008747163,0.001275367,0.0005224639,0.002204522,0.00001617994],"genre_scores_gemma":[0.9926383,0.00009604964,0.005858932,0.000006919674,0.0009930066,0.00006216429,0.0002233497,0.0001088907,0.00001238938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6663042,"threshold_uncertainty_score":0.9999832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09728495395845141,"score_gpt":0.3226472437785373,"score_spread":0.2253622898200859,"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."}}