{"id":"W4323845903","doi":"10.3390/futuretransp3010022","title":"HetroTraffSim: A Macroscopic Heterogeneous Traffic Flow Simulator for Road Bottlenecks","year":2023,"lang":"en","type":"article","venue":"Future Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bottleneck; Traffic flow (computer networking); Traffic congestion reconstruction with Kerner's three-phase theory; Computer science; Traffic wave; Traffic bottleneck; Microscopic traffic flow model; Headway; Floating car data; Traffic generation model; Simulation; Traffic optimization; Traffic simulation; Traffic congestion; Pedestrian; Three-phase traffic theory; Transport engineering; Real-time computing; Microsimulation; Computer network; Engineering; Embedded system","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":[0.0003594542,0.0006171262,0.0006340584,0.0005694928,0.0004226706,0.0006771358,0.001387249,0.0006901721,0.005010232],"category_scores_gemma":[0.0007356496,0.0003202051,0.0007388981,0.0007319897,0.0002963196,0.0007535072,0.0007081466,0.0006868737,0.0007230631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007022811,"about_ca_system_score_gemma":0.001729905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02146208,"about_ca_topic_score_gemma":0.01369675,"domain_scores_codex":[0.9998209,0.00003608046,0.000009785967,0.00002358703,0.00007265863,0.00003702665],"domain_scores_gemma":[0.9997433,0.00007674462,0.00002750311,0.0000309081,0.0000844446,0.00003702449],"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":[0.00005876234,0.00007366991,0.00147477,0.00006315935,0.00003538657,0.00007675223,0.00004899744,0.9672164,0.003148147,0.005773169,0.006897425,0.01513325],"study_design_scores_gemma":[0.00001949078,0.00002394307,0.0002432952,0.000005410511,0.000008291106,0.00001998897,0.00001218107,0.9915462,0.00135369,0.0008363075,0.005919363,0.00001188498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.159103,0.0005161934,0.7619317,0.0005047621,0.0004108856,0.0004994351,0.007100852,0.02119338,0.04873972],"genre_scores_gemma":[0.8500764,0.0006050719,0.1263441,0.0001563852,0.000055931,0.0005432946,0.007656785,0.001353746,0.01320841],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02146208,"threshold_uncertainty_score":0.0426743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006561603593377889,"score_gpt":0.2140174842163822,"score_spread":0.2074558806230043,"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."}}