{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RXQNAGZQNMZS5FBC7MLDOV2DXG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"aeeacac4632b9b727cecafd0581dc061f0f98d31ca288a1850fb2cde23b9cec4","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-11-13T19:38:30Z","title_canon_sha256":"6169bbae5b4eecdd555b1e853eb41424ab022baa5402930e8ea05874445c1c8b"},"schema_version":"1.0","source":{"id":"1911.05777","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.05777","created_at":"2026-07-05T00:19:12Z"},{"alias_kind":"arxiv_version","alias_value":"1911.05777v1","created_at":"2026-07-05T00:19:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.05777","created_at":"2026-07-05T00:19:12Z"},{"alias_kind":"pith_short_12","alias_value":"RXQNAGZQNMZS","created_at":"2026-07-05T00:19:12Z"},{"alias_kind":"pith_short_16","alias_value":"RXQNAGZQNMZS5FBC","created_at":"2026-07-05T00:19:12Z"},{"alias_kind":"pith_short_8","alias_value":"RXQNAGZQ","created_at":"2026-07-05T00:19:12Z"}],"graph_snapshots":[{"event_id":"sha256:591eb0e906cc9f7380b006e018f6220b371e93c25e60d270a65d8bf568289797","target":"graph","created_at":"2026-07-05T00:19:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1911.05777/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the increasing adoption of Automatic Vehicle Location (AVL) and Automatic Passenger Count (APC) technologies by transit agencies, a massive amount of time-stamped and location-based passenger boarding and alighting count data can be collected on a continuous basis. The availability of such large-scale transit data offers new opportunities to produce estimates for Origin-Destination (O-D) flows, helping inform transportation planning and transit management. However, the state-of-the-art methodologies for AVL/APC data analysis mostly tackle the O-D flow estimation problem within routes and ","authors_text":"Pascal Van Hentenryck, Xilei Zhao, Xinyu Liu","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-11-13T19:38:30Z","title":"Optimization Models for Estimating Transit Network Origin-Destination Flows with AVL/APC Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.05777","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b808cad34864b83e92825e4430e78320f0bc76586faffe9e55bd1d69df33b00b","target":"record","created_at":"2026-07-05T00:19:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"aeeacac4632b9b727cecafd0581dc061f0f98d31ca288a1850fb2cde23b9cec4","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-11-13T19:38:30Z","title_canon_sha256":"6169bbae5b4eecdd555b1e853eb41424ab022baa5402930e8ea05874445c1c8b"},"schema_version":"1.0","source":{"id":"1911.05777","kind":"arxiv","version":1}},"canonical_sha256":"8de0d01b306b332e9422fb16375743b999b592899706aaf5a44b99b7401b3bc1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8de0d01b306b332e9422fb16375743b999b592899706aaf5a44b99b7401b3bc1","first_computed_at":"2026-07-05T00:19:12.186777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:19:12.186777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WgJ/z0mCOOECX3AEEqCehDunk1BH5+L9EbHADCL0gLlbxQuyxHmjPWFSLkbzJ2DOUYm42Gj14EdqVZAxB2DGDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:19:12.187170Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.05777","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b808cad34864b83e92825e4430e78320f0bc76586faffe9e55bd1d69df33b00b","sha256:591eb0e906cc9f7380b006e018f6220b371e93c25e60d270a65d8bf568289797"],"state_sha256":"48a3771ccd45d56cdd4f731813c133b89f58ef957cb34577d1c9d6f63cdb77b3"}