{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001309183,0.0001079799,0.0001064291,0.00009143791,0.00007517645,0.00005267543,0.0001107838,0.00003273141,0.00002212817],"category_scores_gemma":[0.000001055593,0.00009248528,0.00001963046,0.0001525252,0.00006357193,0.00006860549,0.00001047814,0.00007001896,0.00006728638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000226364,"about_ca_system_score_gemma":0.00001344539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000100857,"about_ca_topic_score_gemma":0.000008693834,"domain_scores_codex":[0.9992764,0.000003846215,0.00009177614,0.0002394868,0.0001891321,0.0001993317],"domain_scores_gemma":[0.9997864,0.00003501531,0.00001430002,0.0001146632,0.000004265548,0.00004529256],"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":[0.000006348515,0.00001440649,0.000005385075,0.00001796427,0.000003478769,3.16e-7,0.0001046072,0.9580821,0.005179842,0.001079072,0.0001997118,0.03530676],"study_design_scores_gemma":[0.0004767613,0.00004900856,0.0008197951,0.000009533082,0.000007677627,2.014396e-7,0.00009076497,0.9976175,0.00009794724,0.00003780845,0.0006753422,0.0001176094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779655,0.00003098312,0.00131123,0.00006483238,0.0001472133,0.0002736705,0.000001489215,0.0002749846,0.0199301],"genre_scores_gemma":[0.9990287,0.000008634142,0.0006532334,0.0001204107,0.00001675856,0.00002403302,0.000001280855,0.000008234591,0.0001386669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03953544,"threshold_uncertainty_score":0.377144,"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."}}