{"id":"W4413979703","doi":"10.1109/tits.2025.3601536","title":"Real-Time Matching and Dispatching for Urban Freight Transportation: A Hierarchical Reinforcement Learning Through Actor-Critic and H3 Spatial Partitioning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Reinforcement learning; Matching (statistics); Computer science; Transport engineering; Travel time; Artificial intelligence; Operations research; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001976335,0.0004128599,0.0004853174,0.000240828,0.0004807391,0.0001355176,0.0001049815,0.0002185296,0.00005414064],"category_scores_gemma":[0.000003023907,0.0004403742,0.0001610495,0.0002390143,0.0001188911,0.0003265813,2.245782e-7,0.0004522146,0.000006284647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059638,"about_ca_system_score_gemma":0.00003977111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006761202,"about_ca_topic_score_gemma":0.0004361147,"domain_scores_codex":[0.9977967,0.00004853928,0.0009732696,0.0004710569,0.0002937201,0.0004167556],"domain_scores_gemma":[0.9991534,0.0003275379,0.00008241775,0.0001870778,0.00009725765,0.0001523173],"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":[0.0002491757,0.000106527,0.000972011,0.001941716,0.0004521681,0.00001950596,0.0110389,0.9575631,0.004582272,0.02035761,0.0000904866,0.002626512],"study_design_scores_gemma":[0.007645616,0.001461614,0.01399343,0.006005217,0.003078502,0.00003019333,0.008411255,0.8828868,0.04980358,0.006219188,0.01621362,0.004250994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09660079,0.0002841485,0.9001694,0.00005163048,0.0008991918,0.0008604048,0.0002080791,0.0004510406,0.000475295],"genre_scores_gemma":[0.9962637,0.0008924319,0.001379223,0.00003272542,0.00009489545,0.0003641394,0.0003231947,0.00006980942,0.0005798708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8996629,"threshold_uncertainty_score":0.9998048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01690899965056009,"score_gpt":0.2360636984879829,"score_spread":0.2191546988374228,"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."}}