{"id":"W2904042868","doi":"10.1155/2018/3869106","title":"An Improved Deep Learning Model for Traffic Crash Prediction","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Tennessee Department of Transportation; Research and Innovative Technology Administration; U.S. Department of Transportation","keywords":"Computer science; Artificial intelligence; Machine learning; Feature (linguistics); Deep learning; Crash; Autoencoder; Feature learning; Data mining; Supervised learning; Curse of dimensionality; Random forest; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005665378,0.0005640851,0.0005299837,0.0005571513,0.0002443253,0.0005467174,0.001284891,0.0007230951,0.001892416],"category_scores_gemma":[0.001176238,0.0003154588,0.0006270386,0.0005153642,0.0002651451,0.0007900312,0.0007336544,0.001205599,0.0003513785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008797201,"about_ca_system_score_gemma":0.001101263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0264655,"about_ca_topic_score_gemma":0.02280629,"domain_scores_codex":[0.9997662,0.00004327172,0.00001451899,0.0000775475,0.00005132757,0.00004711282],"domain_scores_gemma":[0.9997222,0.00007825571,0.00002851893,0.00001497323,0.0001407063,0.00001537328],"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":[0.00007999119,0.00007763059,0.004592514,0.00003623392,0.0000471397,0.00007368508,0.00003716064,0.9509152,0.001152055,0.003321036,0.002031762,0.03763559],"study_design_scores_gemma":[0.000002145017,0.000006031516,0.0002391587,0.00000163791,0.000004045597,0.00000323437,0.000001540167,0.9990636,0.00007762606,0.0004847541,0.0001144495,0.000001763477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2199833,0.001245017,0.768831,0.001364146,0.0001759101,0.00007508189,0.001483914,0.001274839,0.005566753],"genre_scores_gemma":[0.9663765,0.0003515165,0.0246857,0.000182545,0.00005163639,0.0001230456,0.001252358,0.00003206917,0.006944714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0264655,"threshold_uncertainty_score":0.05262291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00612544029823836,"score_gpt":0.2260695478117455,"score_spread":0.2199441075135071,"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."}}