{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HORTJQ5DGCEQMKNY3UNUT5SNWD","short_pith_number":"pith:HORTJQ5D","canonical_record":{"source":{"id":"1906.04739","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-11T13:42:12Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"40722a1a600c50a5a397fe236e67294a8d0996a43e9d2d72b0bcef6f2ba58cfb","abstract_canon_sha256":"cc13877308945469b53e498103343a8acfbd90f2919b921e438e327788e8e684"},"schema_version":"1.0"},"canonical_sha256":"3ba334c3a330890629b8dd1b49f64db0f3eb80326e72407d459fdd43b035939d","source":{"kind":"arxiv","id":"1906.04739","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04739","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04739v1","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04739","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"HORTJQ5DGCEQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HORTJQ5DGCEQMKNY","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HORTJQ5D","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HORTJQ5DGCEQMKNY3UNUT5SNWD","target":"record","payload":{"canonical_record":{"source":{"id":"1906.04739","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-11T13:42:12Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"40722a1a600c50a5a397fe236e67294a8d0996a43e9d2d72b0bcef6f2ba58cfb","abstract_canon_sha256":"cc13877308945469b53e498103343a8acfbd90f2919b921e438e327788e8e684"},"schema_version":"1.0"},"canonical_sha256":"3ba334c3a330890629b8dd1b49f64db0f3eb80326e72407d459fdd43b035939d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:32.359169Z","signature_b64":"XtPAdYvXTGfs3FpY/5gavcMtSZF0knHeY5U4DszyCMvsIjIv3p/dtkOHrv/CqMehbzVx2zJ7gLfEvMZO9zEfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ba334c3a330890629b8dd1b49f64db0f3eb80326e72407d459fdd43b035939d","last_reissued_at":"2026-05-17T23:43:32.358640Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:32.358640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.04739","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rxya2Vil3JDeF6DX2jVzxTeRouKUNdwE3CQsgOf/g7hbKKrcuvAlvL/6mcHxZotpsqsk4n9xeP0HfgcS1QYLBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:54:48.008913Z"},"content_sha256":"f751b23db35163fcfa60f2447eafb8629a90b367431e256ffd921b95ad698617","schema_version":"1.0","event_id":"sha256:f751b23db35163fcfa60f2447eafb8629a90b367431e256ffd921b95ad698617"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HORTJQ5DGCEQMKNY3UNUT5SNWD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"eess.SP","authors_text":"Adriana-Simona Mihaita, Chen Cai, Sajjad Shafiei","submitted_at":"2019-06-11T13:42:12Z","abstract_excerpt":"The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then considered as an input for a time series OD demand prediction model to support the DTA model for short-term traffic condition forecasting. Results show a high capability of the proposed OD demand estimation method to reduce the DTA model error through an iterative solution algori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04739","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vytFj7lg3MTpbBVLut8jTtgy2iAEQ/ne4G+yZlVQEebkZ3O+CYonY065q9FCV80tRBPhUKpw5rGiwAWGPcTQAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:54:48.009259Z"},"content_sha256":"da90922beb8fda9376f9ad8fb34edab5fe2a3f58128bd70933843cc22da0cb67","schema_version":"1.0","event_id":"sha256:da90922beb8fda9376f9ad8fb34edab5fe2a3f58128bd70933843cc22da0cb67"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/bundle.json","state_url":"https://pith.science/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T03:54:48Z","links":{"resolver":"https://pith.science/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD","bundle":"https://pith.science/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/bundle.json","state":"https://pith.science/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HORTJQ5DGCEQMKNY3UNUT5SNWD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HORTJQ5DGCEQMKNY3UNUT5SNWD","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":"cc13877308945469b53e498103343a8acfbd90f2919b921e438e327788e8e684","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-11T13:42:12Z","title_canon_sha256":"40722a1a600c50a5a397fe236e67294a8d0996a43e9d2d72b0bcef6f2ba58cfb"},"schema_version":"1.0","source":{"id":"1906.04739","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04739","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04739v1","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04739","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"HORTJQ5DGCEQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HORTJQ5DGCEQMKNY","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HORTJQ5D","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:da90922beb8fda9376f9ad8fb34edab5fe2a3f58128bd70933843cc22da0cb67","target":"graph","created_at":"2026-05-17T23:43:32Z","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"},"paper":{"abstract_excerpt":"The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then considered as an input for a time series OD demand prediction model to support the DTA model for short-term traffic condition forecasting. Results show a high capability of the proposed OD demand estimation method to reduce the DTA model error through an iterative solution algori","authors_text":"Adriana-Simona Mihaita, Chen Cai, Sajjad Shafiei","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-11T13:42:12Z","title":"Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04739","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:f751b23db35163fcfa60f2447eafb8629a90b367431e256ffd921b95ad698617","target":"record","created_at":"2026-05-17T23:43:32Z","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":"cc13877308945469b53e498103343a8acfbd90f2919b921e438e327788e8e684","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-06-11T13:42:12Z","title_canon_sha256":"40722a1a600c50a5a397fe236e67294a8d0996a43e9d2d72b0bcef6f2ba58cfb"},"schema_version":"1.0","source":{"id":"1906.04739","kind":"arxiv","version":1}},"canonical_sha256":"3ba334c3a330890629b8dd1b49f64db0f3eb80326e72407d459fdd43b035939d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ba334c3a330890629b8dd1b49f64db0f3eb80326e72407d459fdd43b035939d","first_computed_at":"2026-05-17T23:43:32.358640Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:32.358640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XtPAdYvXTGfs3FpY/5gavcMtSZF0knHeY5U4DszyCMvsIjIv3p/dtkOHrv/CqMehbzVx2zJ7gLfEvMZO9zEfBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:32.359169Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.04739","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f751b23db35163fcfa60f2447eafb8629a90b367431e256ffd921b95ad698617","sha256:da90922beb8fda9376f9ad8fb34edab5fe2a3f58128bd70933843cc22da0cb67"],"state_sha256":"403a6fbf3233b802f8d5e9b497d6d5747d6dac7df1c5a2881dd5b73c65a92de9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZmTIbpcGzoEtjOArryCqCx74ncTMJ0Luls7s1f/TapcI41B7uPNRR9fPSAJoGinJGqNdCh9BgFx8w/hFC0msDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T03:54:48.011225Z","bundle_sha256":"d33cbd4925c1c347db169385ca270b75f93b1562ef589b602441e38f432c8ffc"}}