{"paper":{"title":"Unlocking air traffic flow prediction through microscopic aircraft-state modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Predicting air traffic flow directly from current aircraft states improves accuracy over methods that aggregate past flows.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anqi Liu, Bin Wang, Feng Hong, Guiyuan Jiang, Hina Birahmani, Jiangtao Zhao, Peilan He, Tianrui Li, Yanwei Yu, Yanyong Huang, Yuanyuan Hou","submitted_at":"2026-05-11T07:04:55Z","abstract_excerpt":"Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated time series. However, traffic dynamics are governed by aircraft states and their interactions in continuous airspace. Such aggregation obscures fine-grained information, including aircraft kinematics, boundary interactions, and control intent. Here we present AeroSense, a state-to-flow modeling paradigm that predicts future traffic flow directly from instantaneous airspace situations represented as dynamic sets of airc"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on a large-scale real-world dataset show that AeroSense consistently improves predictive accuracy over aggregation-based forecasting approaches, particularly during high-density traffic periods.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That an end-to-end learned mapping from instantaneous microscopic aircraft states to future regional flow can be established without historical look-back windows and that this mapping preserves all necessary dynamics for accurate prediction.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Predicting air traffic flow directly from current aircraft states improves accuracy over methods that aggregate past flows.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d10e5746dec50779929d9444ecd17199cbfc96859c4a9a7da3f87e6efdafac30"},"source":{"id":"2605.10083","kind":"arxiv","version":2},"verdict":{"id":"cb93be70-f0c6-4118-be1e-64eda7e16be8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T02:54:37.034853Z","strongest_claim":"Experiments on a large-scale real-world dataset show that AeroSense consistently improves predictive accuracy over aggregation-based forecasting approaches, particularly during high-density traffic periods.","one_line_summary":"AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That an end-to-end learned mapping from instantaneous microscopic aircraft states to future regional flow can be established without historical look-back windows and that this mapping preserves all necessary dynamics for accurate prediction.","pith_extraction_headline":"Predicting air traffic flow directly from current aircraft states improves accuracy over methods that aggregate past flows."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.10083/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T06:42:01.059365Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T15:41:12.186341Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T12:01:17.864150Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T09:41:53.935033Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"14fd861570f49a82dab0025f124589f24bb30cacc1d91bab4e258bb7a8c64faa"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a4d82d43506d570827eff5f60dc5f7a4fed3b2c67db6fe8a2f79e6c03cc58703"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}