{"id":"W2959077396","doi":"10.3390/app9142848","title":"A Macroscopic Traffic Model based on Driver Reaction and Traffic Stimuli","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Traffic control and management","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Higher Education Commision, Pakistan; University of Engineering and Technology, Peshawar; Higher Education Commission, Pakistan; University of Engineering and Technology, Lahore","keywords":"Bottleneck; Headway; Microscopic traffic flow model; Traffic bottleneck; Traffic flow (computer networking); Three-phase traffic theory; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic wave; Simulation; Computer science; Traffic model; Traffic generation model; Transport engineering; Engineering; Traffic optimization; Traffic congestion; Real-time computing; Floating car data; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002890997,0.0007961908,0.0007942104,0.0005470184,0.000315541,0.001068385,0.001899399,0.001053829,0.002339143],"category_scores_gemma":[0.0006846511,0.0003341212,0.0006389499,0.000529537,0.0006930818,0.001520918,0.0006255849,0.0009255093,0.0004233692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009425652,"about_ca_system_score_gemma":0.001155295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01069037,"about_ca_topic_score_gemma":0.005653442,"domain_scores_codex":[0.9996578,0.00005828327,0.00001468489,0.0001124753,0.00009901608,0.00005779712],"domain_scores_gemma":[0.9997194,0.00007243872,0.00004762819,0.00003011404,0.00009884435,0.00003155094],"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":[0.00002644632,0.00004523257,0.0007234982,0.00003240064,0.00001445262,0.00008213401,0.00003605334,0.974369,0.003422288,0.0174511,0.0004996737,0.003297601],"study_design_scores_gemma":[0.000003820353,0.00001186943,0.0001714285,0.00000120824,0.000005512889,0.00001084555,0.000004047453,0.9983865,0.0001735103,0.0009881391,0.0002379328,0.00000520103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1320625,0.0002403849,0.8474547,0.0003653287,0.0002557395,0.0001582998,0.0007922082,0.0006733561,0.01799745],"genre_scores_gemma":[0.966796,0.0004966756,0.02063533,0.00008323387,0.00009417145,0.0002538305,0.0006462487,0.00005363804,0.01094077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01069037,"threshold_uncertainty_score":0.02125627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015391871869239,"score_gpt":0.2123614777017342,"score_spread":0.2022075589830418,"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."}}