{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RNFKUHN2SWUUYXJMKI32ZRJBZD","short_pith_number":"pith:RNFKUHN2","schema_version":"1.0","canonical_sha256":"8b4aaa1dba95a94c5d2c5237acc521c8f9a5cd7050f1278df9d9b247d1b182cd","source":{"kind":"arxiv","id":"1810.00777","version":1},"attestation_state":"computed","paper":{"title":"Computing Dynamic User Equilibria on Large-Scale Networks: From Theory to Software Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.AP"],"primary_cat":"math.OC","authors_text":"Gabriel Eve, Ke Han, Terry Friesz","submitted_at":"2018-10-01T15:56:25Z","abstract_excerpt":"Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment, in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper documents theoretical and numerical advances in synthesizing traffic flow theory and DUE modeling, by presenting a holistic computational theory of DUE with numerical implementation encapsulated in a MATLAB software package. In particular, the dynamic netw"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1810.00777","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-01T15:56:25Z","cross_cats_sorted":["math.AP"],"title_canon_sha256":"9b1ceb9071453aff57dee9c3f5f8ebb1f6a194013a48038344eefe8314f928d4","abstract_canon_sha256":"f17e074b67041e48bc23f73725023f49019b2b9e9bfad3a250e1ad55abfc8452"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:24.869867Z","signature_b64":"RoiCk5fGtLz2Ww4ity3I5wcWRqvAXTYKQQxtORu8SGRWht+mpBZIFsFrT0L0KvLMnmkiVXXONcl+ePIAo/U4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b4aaa1dba95a94c5d2c5237acc521c8f9a5cd7050f1278df9d9b247d1b182cd","last_reissued_at":"2026-05-18T00:04:24.869309Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:24.869309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computing Dynamic User Equilibria on Large-Scale Networks: From Theory to Software Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.AP"],"primary_cat":"math.OC","authors_text":"Gabriel Eve, Ke Han, Terry Friesz","submitted_at":"2018-10-01T15:56:25Z","abstract_excerpt":"Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment, in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper documents theoretical and numerical advances in synthesizing traffic flow theory and DUE modeling, by presenting a holistic computational theory of DUE with numerical implementation encapsulated in a MATLAB software package. In particular, the dynamic netw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00777","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1810.00777","created_at":"2026-05-18T00:04:24.869419+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.00777v1","created_at":"2026-05-18T00:04:24.869419+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00777","created_at":"2026-05-18T00:04:24.869419+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNFKUHN2SWUU","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNFKUHN2SWUUYXJM","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNFKUHN2","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD","json":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD.json","graph_json":"https://pith.science/api/pith-number/RNFKUHN2SWUUYXJMKI32ZRJBZD/graph.json","events_json":"https://pith.science/api/pith-number/RNFKUHN2SWUUYXJMKI32ZRJBZD/events.json","paper":"https://pith.science/paper/RNFKUHN2"},"agent_actions":{"view_html":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD","download_json":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD.json","view_paper":"https://pith.science/paper/RNFKUHN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.00777&json=true","fetch_graph":"https://pith.science/api/pith-number/RNFKUHN2SWUUYXJMKI32ZRJBZD/graph.json","fetch_events":"https://pith.science/api/pith-number/RNFKUHN2SWUUYXJMKI32ZRJBZD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD/action/storage_attestation","attest_author":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD/action/author_attestation","sign_citation":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD/action/citation_signature","submit_replication":"https://pith.science/pith/RNFKUHN2SWUUYXJMKI32ZRJBZD/action/replication_record"}},"created_at":"2026-05-18T00:04:24.869419+00:00","updated_at":"2026-05-18T00:04:24.869419+00:00"}