{"id":"W4408280853","doi":"10.1109/tits.2025.3546471","title":"Integrating Node-Place Model With Shapley Additive Explanation for Metro Ridership Regression","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Beijing Municipal Natural Science Foundation","keywords":"Computer science; Node (physics); Regression analysis; Econometrics; Transport engineering; Engineering; Mathematics; Machine learning","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":[0.0002067998,0.0002853232,0.0002633528,0.0005608567,0.0001795595,0.00006946949,0.0001396304,0.0001440119,0.00001533737],"category_scores_gemma":[0.000003230974,0.0002523564,0.0001266484,0.0004070895,0.00003037065,0.0002897145,1.49236e-7,0.0002346228,0.000006194762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002091124,"about_ca_system_score_gemma":0.00003645569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003798971,"about_ca_topic_score_gemma":0.0004296151,"domain_scores_codex":[0.9986129,0.00003092468,0.0005330304,0.0003232307,0.0002673283,0.0002325763],"domain_scores_gemma":[0.9993293,0.0001407856,0.0000806858,0.0002099051,0.0001719934,0.00006738884],"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":[0.0001873595,0.00007021486,0.0000103567,0.0003179232,0.0002006189,9.700713e-7,0.0007994311,0.9742009,0.0004278136,0.003692699,0.007129138,0.01296258],"study_design_scores_gemma":[0.0005568638,0.0001183035,0.00002214173,0.0007745675,0.0001542973,5.571127e-7,0.002907493,0.9457534,0.04557994,0.00004143654,0.003816402,0.0002745862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00300688,0.0000790273,0.9899921,0.00008482069,0.0008986057,0.001252192,0.000364349,0.002765597,0.001556422],"genre_scores_gemma":[0.9934643,0.0001809106,0.003774034,0.00005674371,0.00002480683,0.001368215,0.0001724367,0.00004605452,0.0009125073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9904574,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154605263799243,"score_gpt":0.255008262620612,"score_spread":0.2334622099826196,"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."}}