{"id":"W94444423","doi":"10.1007/978-1-4419-6142-6_9","title":"Traffic Simulation with Dynameq","year":2010,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Traffic control and management","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; Inro Consultants (Canada)","funders":"","keywords":"Computer science; Path (computing); Traffic congestion; Traffic simulation; Operations research; Calibration; Travel time; Network traffic simulation; Software; Mathematical optimization; Simulation software; Simulation; Transport engineering; Engineering; Network traffic control; Mathematics; Computer network; Microsimulation","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":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.009625958,0.000787571,0.0005302774,0.0130377,0.002436875,0.004392481,0.006821007,0.0002599835,0.002005037],"category_scores_gemma":[0.0003816653,0.0007825258,0.0001310412,0.004279053,0.007349932,0.006814878,0.002857932,0.002542446,0.0004622186],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004529622,"about_ca_system_score_gemma":0.0004923523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002142259,"about_ca_topic_score_gemma":0.01691637,"domain_scores_codex":[0.9842159,0.0001440599,0.001462536,0.002330953,0.009858697,0.001987909],"domain_scores_gemma":[0.9952028,0.0001847628,0.00006854322,0.001673969,0.002461401,0.0004084592],"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":[0.0001135221,0.0001438794,0.0000210091,0.00006755078,0.0001105418,0.0002014154,0.0003858024,0.6214334,0.0002511399,0.3691543,0.0002621239,0.007855385],"study_design_scores_gemma":[0.001777446,0.0002673123,0.003060709,0.0008205484,0.00003063318,0.00003162012,0.002723677,0.6454608,0.0001310229,0.004990283,0.3393908,0.001315146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01067908,0.0001258206,0.002834411,0.00773601,0.003986602,0.005546184,0.0001582016,0.0004630878,0.9684706],"genre_scores_gemma":[0.6923071,0.003538596,0.01101874,0.00009017358,0.0003734249,0.001703283,0.0003166818,0.0001435406,0.2905085],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6816279,"threshold_uncertainty_score":0.9997587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03093674975654856,"score_gpt":0.3528278560513515,"score_spread":0.3218911062948029,"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."}}