{"id":"W3036285880","doi":"10.1155/2020/8237649","title":"Review of Virtual Traffic Simulation and Its Applications","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; Ningbo University","keywords":"Traffic simulation; Status quo; Computer science; Traffic flow (computer networking); Transport engineering; Animation; Hotspot (geology); Traffic conflict; Crowd simulation; Network traffic simulation; Traffic engineering; Traffic generation model; Floating car data; Virtual reality; Modeling and simulation; Simulation; Traffic congestion; Engineering; Human–computer interaction; Network traffic control; Microsimulation; Crowds; Computer security; Real-time computing; Computer network; Computer graphics (images)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005424313,0.0000515057,0.0001318415,0.00003077582,0.00001089299,0.00000230107,0.00003412391,0.00002182268,0.00001224858],"category_scores_gemma":[0.00001898285,0.00005139746,0.00004033482,0.0001411273,0.000005855568,0.0002088448,3.108642e-7,0.00007226979,9.887506e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008782325,"about_ca_system_score_gemma":0.00001098898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.916639e-8,"about_ca_topic_score_gemma":7.604753e-7,"domain_scores_codex":[0.9994296,0.000005872772,0.0003833366,0.00003947491,0.0001041539,0.00003752567],"domain_scores_gemma":[0.9996069,0.00003547994,0.0001402906,0.00002653751,0.0001404266,0.00005037517],"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":[0.00001113672,0.000006694504,0.00001038998,0.001067859,0.00001115994,4.238364e-7,0.0003575608,0.9733952,0.003066095,0.0002648108,0.00001080191,0.02179785],"study_design_scores_gemma":[0.00271518,0.0005839033,0.0170408,0.003960485,0.0004026545,0.000008783784,0.0008027374,0.946201,0.003043746,0.0002342724,0.02456166,0.0004447504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6765112,0.03504609,0.2872183,0.0005495729,0.0001100254,0.0004010167,0.00003415653,0.00006262367,0.00006699739],"genre_scores_gemma":[0.9843502,0.01408204,0.001355617,0.0001460784,0.00003435942,0.000002725345,0.00001877428,0.000008714964,0.000001475596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.307839,"threshold_uncertainty_score":0.2095927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251582154380633,"score_gpt":0.2688675303703813,"score_spread":0.256351708826575,"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."}}