{"id":"W4225371371","doi":"10.32473/flairs.v35i.130731","title":"Pedestrian Traffic Prediction using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Pedestrian; Artificial intelligence; Artificial neural network; Event (particle physics); Traffic flow (computer networking); Deep learning; Dual (grammatical number); Pedestrian detection; Machine learning; Engineering; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000285242,0.001017359,0.0004369612,0.001830333,0.0002907964,0.0005894138,0.0005819729,0.0005757882,0.002137664],"category_scores_gemma":[0.0009281152,0.0004105318,0.0005620444,0.001088644,0.0001704162,0.000905535,0.0004940392,0.0006920911,0.0007963224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012028,"about_ca_system_score_gemma":0.0006772443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02149879,"about_ca_topic_score_gemma":0.02359715,"domain_scores_codex":[0.9998267,0.00002298879,0.000006442496,0.00005180832,0.0000395503,0.00005250788],"domain_scores_gemma":[0.9997109,0.0000627982,0.00004403942,0.00002418571,0.000129067,0.00002902141],"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":[0.0003033073,0.0004710454,0.02586503,0.00007638193,0.0001107261,0.0002003643,0.00005575425,0.6513325,0.004888281,0.00222176,0.01111864,0.3033562],"study_design_scores_gemma":[0.000001765491,0.0000102445,0.001126235,0.000004361056,0.00000478404,0.000009749701,0.000006577297,0.9973694,0.0005248301,0.0006422793,0.0002970133,0.000002744444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5032452,0.0009781377,0.475297,0.0006959889,0.0003741403,0.00009656663,0.002534434,0.005964403,0.01081412],"genre_scores_gemma":[0.9587508,0.0003072913,0.0333237,0.00009247664,0.0000721888,0.00004017842,0.002654279,0.00004669379,0.004712413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02149879,"threshold_uncertainty_score":0.04274732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1045126509968553,"score_gpt":0.3240030754447231,"score_spread":0.2194904244478678,"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."}}