{"id":"W4403955213","doi":"10.32604/cmc.2024.058888","title":"Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review","year":2024,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"Silesian University of Technology","keywords":"Discrete choice; Mode choice; Mode (computer interface); Computer science; Artificial intelligence; Machine learning; Engineering; Public transport; Transport engineering; Human–computer interaction","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"],"consensus_categories":[],"category_scores_codex":[0.00220392,0.0009469886,0.002324548,0.0003538623,0.0001978003,0.0008257056,0.001246984,0.0003066931,0.00003608382],"category_scores_gemma":[0.0001125803,0.0007765262,0.0003277189,0.0002949622,0.0002475278,0.0008847441,0.0004117878,0.0002371818,0.00001331484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078577,"about_ca_system_score_gemma":0.00004483541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001605932,"about_ca_topic_score_gemma":0.00005109699,"domain_scores_codex":[0.9943174,0.0003215022,0.003010737,0.001025659,0.0004764105,0.0008482714],"domain_scores_gemma":[0.997297,0.0006901172,0.0005915213,0.001039236,0.0002147589,0.0001672959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002604217,0.0002454947,0.00009184467,0.6484076,0.00198779,0.0001114775,0.003690828,0.0008868127,0.2465002,0.01512388,0.02051553,0.06217808],"study_design_scores_gemma":[0.0006435173,0.0005099443,0.0003360349,0.20734,0.002522925,0.0001612461,0.0001733235,0.2550512,0.5237048,0.002792757,0.004196199,0.002568037],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1717799,0.0056496,0.7962065,0.0003319652,0.007248378,0.009736408,0.0008128017,0.008103886,0.0001305918],"genre_scores_gemma":[0.9827604,0.002998888,0.01132066,0.0002746039,0.0009736977,0.001290336,0.0001254556,0.0002069532,0.00004898376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8109806,"threshold_uncertainty_score":0.9994686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201433426614659,"score_gpt":0.2712776441371779,"score_spread":0.251134301475712,"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."}}