{"id":"W4389097742","doi":"10.1177/03611981231211317","title":"Simulation of Signalized Intersection with Non-Lane-Based Heterogeneous Traffic Conditions Using Cellular Automata","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic control and management","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Cellular automaton; Benchmark (surveying); Calibration; Computer science; Field (mathematics); Traffic simulation; Simulation; Mean squared error; Statistics; Transport engineering; Mathematics; Algorithm; Engineering; Geography; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003122956,0.0005843427,0.0005613517,0.0006687097,0.0005557582,0.001011338,0.0008976386,0.0009509769,0.001055027],"category_scores_gemma":[0.001046495,0.0003132871,0.0006968367,0.0006499779,0.0007030877,0.0006246364,0.0006221122,0.0005711213,0.0001232791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382392,"about_ca_system_score_gemma":0.000948822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04121918,"about_ca_topic_score_gemma":0.02282451,"domain_scores_codex":[0.99968,0.00009258895,0.00001612074,0.00006247645,0.00004639434,0.0001025221],"domain_scores_gemma":[0.9991603,0.0004340028,0.0001171239,0.00006086467,0.0001549887,0.00007286472],"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":[0.00002316368,0.00002414297,0.00232026,0.000005313329,0.000008628916,0.00003734974,0.00001950847,0.9964066,0.0003691177,0.0003506505,0.00003231343,0.0004029049],"study_design_scores_gemma":[0.000004930742,0.00002437871,0.0006336221,0.000001182981,0.000005599596,0.000004516512,0.00002730219,0.9988803,0.0002665924,0.0001054548,0.00004223354,0.000003905168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985064,0.00003863596,0.0116755,0.00004769116,0.00001571289,0.00002560094,0.0002200631,0.0001051383,0.002807746],"genre_scores_gemma":[0.9985046,0.0000206809,0.001005005,0.000003432508,0.000001490194,0.00001320761,0.00007615406,0.000003451777,0.0003718529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04121918,"threshold_uncertainty_score":0.08195853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06063072301889486,"score_gpt":0.3397556030578688,"score_spread":0.279124880038974,"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."}}