{"id":"W2382852895","doi":"10.3141/2540-03","title":"Use of Agent-Based Crowd Simulation to Investigate the Performance of Large-Scale Intermodal Facilities: Case Study of Union Station in Toronto, Ontario, Canada","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Arup Group (Canada); University of Toronto","funders":"","keywords":"Microsimulation; Transport engineering; Transit (satellite); Plan (archaeology); Scale (ratio); Traffic congestion; Pedestrian; Public transport; Operations research; Engineering; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002560227,0.0001654636,0.00035052,0.000452781,0.0001297381,0.00002210898,0.0004208999,0.00008192933,0.0002010075],"category_scores_gemma":[0.0001198623,0.0001088346,0.0001107746,0.0008667885,0.00018206,0.000600125,0.000005201836,0.0005460613,4.837589e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000974949,"about_ca_system_score_gemma":0.0009884512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8865342,"about_ca_topic_score_gemma":0.9984922,"domain_scores_codex":[0.9951493,0.0007170184,0.001529732,0.0001830756,0.002027105,0.0003937779],"domain_scores_gemma":[0.9960064,0.001019602,0.0003280812,0.0003695662,0.002109546,0.0001667782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007115089,0.0002150391,0.382332,0.0003056328,0.00006944909,0.0000390307,0.01797304,0.5912999,0.004514384,0.00005066603,0.0001524153,0.002336982],"study_design_scores_gemma":[0.002023665,0.0009889434,0.924249,0.0004943607,0.00003062302,4.97108e-7,0.02150428,0.0460876,0.003962313,0.00003296172,0.0004836355,0.000142144],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950324,0.0000201778,0.003347732,0.0002229538,0.0001738753,0.0009760413,0.0002061873,0.000008034775,0.00001254322],"genre_scores_gemma":[0.9993123,0.00005537022,0.0003704006,0.000009842819,0.00001299177,0.0000351684,0.00001171335,0.00002544331,0.0001667609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5452123,"threshold_uncertainty_score":0.4438145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06895091254402216,"score_gpt":0.34071203158368,"score_spread":0.2717611190396578,"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."}}