{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007258172,0.0001993216,0.0001998191,0.0001152856,0.0000800202,0.00002802152,0.0001031074,0.0001040495,0.00003945077],"category_scores_gemma":[0.000002171888,0.0002010146,0.0001479066,0.0002521866,0.000009772002,0.00008169275,0.000001151632,0.00008068256,0.00004993146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003259794,"about_ca_system_score_gemma":0.000009774738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002630692,"about_ca_topic_score_gemma":0.0003739855,"domain_scores_codex":[0.9990113,0.000006749354,0.0002553459,0.0002283654,0.000158533,0.0003396983],"domain_scores_gemma":[0.999684,0.00002275154,0.00002309918,0.0001657425,0.00002755462,0.00007680659],"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":[0.00002722971,0.00001375041,0.00002412565,0.0001976069,0.00006470107,0.00001265754,0.0005557476,0.9046538,0.0004248479,0.00005093569,0.0019782,0.0919964],"study_design_scores_gemma":[0.002373065,0.0001288567,0.03629205,0.00003617966,0.0001386065,0.000001018611,0.0002011851,0.8501508,0.0003223589,0.00004357663,0.1098583,0.0004540412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815375,0.000492955,0.0117208,0.000269573,0.002289743,0.0009688739,0.0002504981,0.002419415,0.00005064596],"genre_scores_gemma":[0.9975262,0.0001007013,0.000626385,0.00005496178,0.0005437945,0.0002614403,0.000664089,0.00005912224,0.0001633502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1078801,"threshold_uncertainty_score":0.8197136,"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."}}