{"id":"W2997595517","doi":"10.1155/2019/8491042","title":"Improving Pedestrian Hybrid Beacon Crosswalk by Using Upstream Detection Strategy","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Government of Jiangsu Province; Nanjing Forestry University","keywords":"Schema crosswalk; Pedestrian; Upstream (networking); VisSim; Signal timing; Computer science; Pedestrian crossing; Simulation; Algorithm; Real-time computing; Transport engineering; Microsimulation; Engineering; Traffic signal; Telecommunications","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":[0.0003944013,0.0007980319,0.0005258765,0.0007485935,0.0003935382,0.0005494459,0.0009704891,0.0003957681,0.002333925],"category_scores_gemma":[0.0009874165,0.0003574436,0.0004499738,0.0003927585,0.000231878,0.0006543355,0.0008911289,0.0003714499,0.0005235174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005041007,"about_ca_system_score_gemma":0.0009631778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006174017,"about_ca_topic_score_gemma":0.00715111,"domain_scores_codex":[0.9996474,0.00006867029,0.00001308953,0.00008790027,0.00009806302,0.00008491237],"domain_scores_gemma":[0.9994962,0.0001285424,0.00007287991,0.00005069887,0.0001656683,0.00008595226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007884181,0.0009548733,0.0208529,0.0003859222,0.0001091627,0.0005557338,0.0005208124,0.5333832,0.120987,0.008550671,0.002502333,0.310409],"study_design_scores_gemma":[0.00007299961,0.0008611893,0.004832194,0.00002100807,0.0001087451,0.0001757703,0.0002211833,0.9670141,0.02241306,0.001018515,0.003210191,0.00005093541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3747576,0.0004271007,0.6142112,0.0001303719,0.000091001,0.0001066196,0.00008044554,0.001782762,0.008412794],"genre_scores_gemma":[0.9566126,0.000126649,0.04102683,0.00003985321,0.000007352204,0.00004592898,0.0000989277,0.00003953945,0.002002227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006174017,"threshold_uncertainty_score":0.01227617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005398394073131665,"score_gpt":0.2248572858039961,"score_spread":0.2194588917308645,"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."}}