{"id":"W2611377974","doi":"10.5539/mas.v11n7p1","title":"Optimization of Design Scheme for Toll Plaza Based on M/M/C Queuing Theory and Cellular Automata Simulation Algorithm","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Traffic control and management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Toll; Queueing theory; Computer science; Cellular automaton; Throughput; SAFER; Robustness (evolution); Scheme (mathematics); Algorithm; Operations research; Computer network; Telecommunications; Engineering; Computer security; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005363359,0.0009303045,0.0005852199,0.000930125,0.0007145992,0.001073756,0.0009771716,0.0007741038,0.003851743],"category_scores_gemma":[0.001257938,0.0003314366,0.0008199102,0.0005727346,0.0006216493,0.0007755579,0.0007459774,0.0005679021,0.0003847475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001384021,"about_ca_system_score_gemma":0.001277875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006136286,"about_ca_topic_score_gemma":0.004732987,"domain_scores_codex":[0.999441,0.0001441384,0.00003295466,0.0001218541,0.0001474923,0.0001125561],"domain_scores_gemma":[0.9995403,0.0001215162,0.00008764493,0.00004657718,0.0001681627,0.00003582112],"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":[0.0001168462,0.00004475641,0.001048266,0.000125134,0.00003579375,0.00008388025,0.00006287429,0.9436806,0.01311994,0.01187582,0.001014507,0.02879146],"study_design_scores_gemma":[0.0000197798,0.00009430097,0.0002477132,0.000009552395,0.0000265636,0.00003185821,0.00002455658,0.9944405,0.00223668,0.001731726,0.001123772,0.00001299108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05268396,0.0002235748,0.9361256,0.0001372837,0.00006817743,0.0001483568,0.00005783771,0.0005407361,0.01001439],"genre_scores_gemma":[0.9243351,0.0001636779,0.07232362,0.00004779188,0.00001324664,0.000160494,0.00006663248,0.00003695564,0.00285242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006136286,"threshold_uncertainty_score":0.01288533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640043112780853,"score_gpt":0.2312986361514775,"score_spread":0.214898205023669,"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."}}