{"id":"W4379116774","doi":"10.1109/tits.2023.3279929","title":"GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Graph; Convolutional neural network; Artificial intelligence; Theoretical computer science","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":[0.0003279777,0.0007700693,0.0004123629,0.0008736374,0.0002951214,0.0004824906,0.0009174109,0.0005867271,0.001542414],"category_scores_gemma":[0.001346875,0.0003107725,0.000536328,0.001134804,0.0003288961,0.001449359,0.0005883415,0.001175447,0.0003074788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426259,"about_ca_system_score_gemma":0.001312608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03663796,"about_ca_topic_score_gemma":0.04293046,"domain_scores_codex":[0.9998313,0.00002201519,0.000007978851,0.00006018616,0.00004362627,0.00003487506],"domain_scores_gemma":[0.9997151,0.00009460824,0.00004841577,0.00003540572,0.00008437767,0.0000220809],"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":[0.00007522861,0.00005366711,0.002514082,0.00003674899,0.00003862354,0.0000609514,0.00002451258,0.9232302,0.002674654,0.008029657,0.002728803,0.06053293],"study_design_scores_gemma":[7.537317e-7,0.000002820235,0.0001378073,0.000001138543,0.000002408466,0.00000394166,0.000001238765,0.9981127,0.000220206,0.001366141,0.0001492533,0.000001584426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08905067,0.000930119,0.9018033,0.0006428178,0.0001066923,0.00004467128,0.001203271,0.002695413,0.003523129],"genre_scores_gemma":[0.9065831,0.0007776339,0.08612587,0.0001474186,0.00005237991,0.00006755212,0.002175965,0.0001066384,0.003963456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03663796,"threshold_uncertainty_score":0.07284939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655555051913524,"score_gpt":0.2314106479643883,"score_spread":0.2148550974452531,"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."}}