{"id":"W2150670932","doi":"10.1139/cjce-2013-0331","title":"Optimizing route choice in multimodal transportation networks","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Routing (electronic design automation); Transport engineering; Public transport; Traffic congestion; Flow network; Mode (computer interface); Markov chain; Process (computing); Multimodal transport; Markov process; Operations research; Transportation planning; Service (business); Engineering; Computer network; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001338596,0.000968791,0.0009903884,0.001165205,0.0005324751,0.000952223,0.001013603,0.001007011,0.001859227],"category_scores_gemma":[0.003371254,0.0006163509,0.0006322745,0.001155713,0.0008116041,0.001063059,0.0008060464,0.0005449519,0.00015799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002455554,"about_ca_system_score_gemma":0.001389297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0251612,"about_ca_topic_score_gemma":0.02133877,"domain_scores_codex":[0.9993533,0.0003221635,0.00001790172,0.0001250827,0.0000596021,0.0001219835],"domain_scores_gemma":[0.9988136,0.0008104076,0.0001545002,0.00003233766,0.0001027441,0.00008639444],"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":[0.00001821958,0.00001254285,0.0003501562,0.00001358831,0.00001484699,0.00001776383,0.0000132784,0.9948093,0.0001948076,0.001154206,0.00007417957,0.003327157],"study_design_scores_gemma":[0.000005497003,0.0000215131,0.0001495239,0.000002438801,0.000006446586,0.000003931121,0.00001511435,0.9980981,0.00009257988,0.001485815,0.0001158984,0.000003111267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3466187,0.0006941786,0.6470059,0.0003115381,0.0000320266,0.0001598939,0.0002199537,0.0002348948,0.004722958],"genre_scores_gemma":[0.9421736,0.0002629525,0.0549948,0.0000295038,0.00001051746,0.0001102193,0.0001217914,0.00003777194,0.002258701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0251612,"threshold_uncertainty_score":0.05002952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007589051931459066,"score_gpt":0.2140883705390273,"score_spread":0.2064993186075682,"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."}}