{"id":"W4404042228","doi":"10.1080/15472450.2024.2417175","title":"Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Term (time); Time series; Series (stratigraphy); Computer science; Transport engineering; Engineering; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003698055,0.001034507,0.0007597394,0.002552354,0.0002254271,0.001127674,0.000713165,0.000804981,0.0006735624],"category_scores_gemma":[0.007634351,0.0002346402,0.0009686213,0.001630018,0.0002034481,0.001881716,0.000436146,0.0007546745,0.00022475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004847927,"about_ca_system_score_gemma":0.0005911629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01519266,"about_ca_topic_score_gemma":0.009412149,"domain_scores_codex":[0.9990475,0.0003458372,0.0000926797,0.0001844617,0.000259635,0.00006996685],"domain_scores_gemma":[0.9954723,0.00344921,0.0002364044,0.0002289139,0.0005184934,0.00009468201],"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":[0.0006856884,0.000420499,0.06498668,0.0003391918,0.000715108,0.0002036872,0.0002523363,0.5386752,0.002593923,0.003971411,0.001564854,0.3855913],"study_design_scores_gemma":[0.000005747864,0.00006891681,0.008512103,0.0000219609,0.00003757283,0.00002220551,0.00007643199,0.9895566,0.0004999721,0.0006205992,0.0005617182,0.00001618744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6557854,0.007791544,0.3265237,0.001178384,0.000378078,0.0001713779,0.001212686,0.0009510096,0.006007772],"genre_scores_gemma":[0.9455143,0.002612899,0.04960717,0.00007000052,0.000161912,0.0000651634,0.001142335,0.00005620834,0.0007699995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01519266,"threshold_uncertainty_score":0.03020847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1758329222664913,"score_gpt":0.4386914603582576,"score_spread":0.2628585380917663,"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."}}