{"id":"W2086181527","doi":"10.3141/1876-11","title":"Calibration and Application of a Simulation-Based Dynamic Traffic Assignment Model","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Inro Consultants (Canada)","funders":"","keywords":"Calibration; Traffic simulation; Traffic flow (computer networking); Simulation; Set (abstract data type); Computer science; Subnetwork; Matrix (chemical analysis); Representation (politics); Traffic generation model; Algorithm; Simulation modeling; Basis (linear algebra); Data set; Trip distribution; Intersection (aeronautics); Mathematics; Real-time computing; Statistics; Engineering; Transport engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00393131,0.0001852725,0.0003390798,0.000855002,0.0009413381,0.00009703245,0.00049954,0.0002316569,0.00003894118],"category_scores_gemma":[0.000240285,0.0001599567,0.0002112503,0.001811516,0.0009575784,0.0007022512,0.000002229699,0.0008941781,0.000001925054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003310393,"about_ca_system_score_gemma":0.001545794,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006833187,"about_ca_topic_score_gemma":0.0615123,"domain_scores_codex":[0.9938134,0.0007811332,0.001251966,0.0003439883,0.003277937,0.0005315919],"domain_scores_gemma":[0.9950188,0.00107232,0.000621293,0.0002567518,0.002724586,0.000306209],"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":[0.0004476483,0.0002097484,0.01626991,0.0001092758,0.00003591177,0.000005201962,0.009966932,0.9630438,0.0008311395,0.005296316,0.00003977152,0.003744349],"study_design_scores_gemma":[0.004295358,0.0006512558,0.329691,0.0005873159,0.0001415853,1.151416e-7,0.006245236,0.64512,0.0009155636,0.01014706,0.001799418,0.0004061113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6135225,0.00009378584,0.3826261,0.002704754,0.0001109733,0.0008240711,0.00004793783,0.00002898022,0.00004096251],"genre_scores_gemma":[0.9924068,0.0003169814,0.00692292,0.00003954645,0.00005427327,0.00005747066,0.00004766335,0.00003431896,0.0001200163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3788843,"threshold_uncertainty_score":0.9997804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06003143686512434,"score_gpt":0.3995292099768136,"score_spread":0.3394977731116893,"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."}}