{"id":"W1934735491","doi":"10.1002/atr.1276","title":"A reinforcement learning approach for distance‐based dynamic tolling in the stochastic network environment","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Toll; Reinforcement learning; Computer science; Benchmark (surveying); Throughput; Markov chain; Markov decision process; Q-learning; Mathematical optimization; Markov process; Simulation; Artificial intelligence; Mathematics; Machine learning; 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":[0.001085842,0.0005118013,0.0008427126,0.0003753212,0.0003570878,0.0006228671,0.00125472,0.000757416,0.002182066],"category_scores_gemma":[0.002294786,0.0003187289,0.0004981679,0.0002888349,0.000931217,0.0008555256,0.0008971714,0.001088471,0.0001797513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408144,"about_ca_system_score_gemma":0.001089007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00957006,"about_ca_topic_score_gemma":0.005521643,"domain_scores_codex":[0.9994687,0.0002213459,0.00001968243,0.0001008904,0.000108675,0.00008073051],"domain_scores_gemma":[0.9989274,0.0006054502,0.0001450722,0.0000540714,0.0001827052,0.00008534651],"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":[0.00001073788,0.00001428344,0.0001596401,0.000007653484,0.00000822975,0.00001833931,0.00001116925,0.9909765,0.0001835278,0.004824914,0.00009149017,0.003693581],"study_design_scores_gemma":[0.000002174682,0.0000049448,0.00001988356,7.366863e-7,0.000001083281,0.000002255008,0.000001455403,0.9986445,0.00002848667,0.001230894,0.00006233083,0.000001257755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02931081,0.0001161241,0.966467,0.0002655968,0.00003382122,0.00003263489,0.00002674081,0.0001348085,0.003612363],"genre_scores_gemma":[0.9535533,0.00009547387,0.04309409,0.00006400704,0.00003026012,0.00007481792,0.00002890777,0.00002671093,0.003032393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00957006,"threshold_uncertainty_score":0.01902872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004851720496764469,"score_gpt":0.1931793740494931,"score_spread":0.1883276535527287,"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."}}