{"id":"W2916752133","doi":"10.1155/2019/4145353","title":"Spatiotemporal Traffic Flow Prediction with KNN and LSTM","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":281,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Autoregressive integrated moving average; Computer science; Traffic flow (computer networking); Support vector machine; Data mining; Autoregressive model; Weighting; Artificial intelligence; Intelligent transportation system; Artificial neural network; Time series; Machine learning; Engineering; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006752091,0.00008093484,0.0001172402,0.0001173117,0.00001649393,0.00001188364,0.00003431665,0.00003578061,0.000008934711],"category_scores_gemma":[0.000001025936,0.00006976575,0.00002847874,0.00008987937,0.00001124203,0.000552331,4.090542e-7,0.0001154591,0.000001221204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002306371,"about_ca_system_score_gemma":0.000007817664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.050848e-7,"about_ca_topic_score_gemma":0.00001552965,"domain_scores_codex":[0.9994645,0.000004997395,0.0002405082,0.00006432113,0.0001525653,0.00007309501],"domain_scores_gemma":[0.9997685,0.000007344211,0.00008002258,0.00005237024,0.00004960036,0.00004214625],"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":[0.00009488899,0.0000223655,0.00195979,0.0001026985,0.00005273973,0.000009064352,0.0006743908,0.9004397,0.001800952,0.00009064661,0.0004518932,0.09430084],"study_design_scores_gemma":[0.006220314,0.002054517,0.7841593,0.0005960516,0.0002505858,0.00007409061,0.001193779,0.1680447,0.004123458,0.0001159012,0.03266764,0.0004996638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9175384,0.00009868701,0.08125503,0.00005077289,0.0003158364,0.0001507124,0.000008243587,0.000426458,0.0001558334],"genre_scores_gemma":[0.9876398,0.0003303131,0.01191709,0.0000156457,0.00004252432,0.000002932161,0.00001887707,0.00001515671,0.00001768756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7821996,"threshold_uncertainty_score":0.2844965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002756394652977247,"score_gpt":0.1781804182304164,"score_spread":0.1754240235774391,"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."}}