{"id":"W2942290672","doi":"10.1109/itsc.2019.8916908","title":"DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interpretability; Computer science; Pedestrian; Hazard; Block (permutation group theory); Curse of dimensionality; Deep learning; Exploit; Artificial intelligence; Artificial neural network; Index (typography); Data mining; Machine learning; Engineering; Mathematics; 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":[0.0008321254,0.0006992175,0.0004921638,0.0006805391,0.0002125495,0.0004897168,0.001060979,0.0005410851,0.00210209],"category_scores_gemma":[0.002191981,0.0002759745,0.0005099171,0.0004809205,0.000215756,0.0006465976,0.0009022506,0.0009280089,0.0003951561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005850819,"about_ca_system_score_gemma":0.0007768032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0159473,"about_ca_topic_score_gemma":0.01768616,"domain_scores_codex":[0.9998183,0.00004341643,0.000007699129,0.00005601874,0.00002543155,0.00004924955],"domain_scores_gemma":[0.9994889,0.0002184621,0.00007259704,0.00004482378,0.0001017158,0.00007344523],"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":[0.001118794,0.0005888996,0.1180263,0.0001684587,0.0002894736,0.0003595582,0.0002834999,0.6176323,0.003740289,0.005688972,0.008530457,0.243573],"study_design_scores_gemma":[0.000007809575,0.00004337571,0.004180701,0.000006960846,0.00001363728,0.00002591987,0.00002822258,0.9931594,0.0003941609,0.001818583,0.000313987,0.0000071683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6008125,0.0006007141,0.3924495,0.0004922864,0.0001130689,0.0000597267,0.00234625,0.001698907,0.001427007],"genre_scores_gemma":[0.9714339,0.000119851,0.02397254,0.00005715406,0.00003165898,0.00004800286,0.002058008,0.00003782909,0.002241088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0159473,"threshold_uncertainty_score":0.03170896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657432001340336,"score_gpt":0.2538640098064884,"score_spread":0.2372896897930851,"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."}}