{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6SDD4FDINJHIOMKNUA5Q3CF2WL","short_pith_number":"pith:6SDD4FDI","schema_version":"1.0","canonical_sha256":"f4863e14686a4e87314da03b0d88bab2ffbf482fe4f2b0ff9b89b7c0fb1a9ec6","source":{"kind":"arxiv","id":"2504.18008","version":2},"attestation_state":"computed","paper":{"title":"TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jeremy Dilmore, Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka","submitted_at":"2025-04-25T01:28:32Z","abstract_excerpt":"Urban congestion at signalized intersections leads to significant delays, economic losses, and increased emissions. Existing deep learning models often lack spatial generalizability, rely on complex architectures, and struggle with real-time deployment. To address these limitations, we propose the Temporal Graph-based Digital Twin (TGDT), a scalable framework that integrates Temporal Convolutional Networks and Attentional Graph Neural Networks for dynamic, direction-aware traffic modeling and assessment at urban corridors. TGDT estimates key Measures of Effectiveness (MOEs) for traffic flow op"},"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":"2504.18008","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-25T01:28:32Z","cross_cats_sorted":[],"title_canon_sha256":"c6cfd4dcd9a7664c48cd1c2b325ab979b0f5435efd04944206d84f9d61aafd22","abstract_canon_sha256":"a1e88ed1d85177fb37a0be2509c018321dfc45a56f6c1b44fd0481dc61f4b242"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:23.632340Z","signature_b64":"9UcqbGZqiUh/leoSRgfILrUHZ7RtPHK2xvYFHbT83MGngiHvii/Lv3P6+7GOL7zRf8YTXsYpDQst7NCAL7bHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4863e14686a4e87314da03b0d88bab2ffbf482fe4f2b0ff9b89b7c0fb1a9ec6","last_reissued_at":"2026-07-05T11:03:23.631931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:23.631931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jeremy Dilmore, Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka","submitted_at":"2025-04-25T01:28:32Z","abstract_excerpt":"Urban congestion at signalized intersections leads to significant delays, economic losses, and increased emissions. Existing deep learning models often lack spatial generalizability, rely on complex architectures, and struggle with real-time deployment. To address these limitations, we propose the Temporal Graph-based Digital Twin (TGDT), a scalable framework that integrates Temporal Convolutional Networks and Attentional Graph Neural Networks for dynamic, direction-aware traffic modeling and assessment at urban corridors. TGDT estimates key Measures of Effectiveness (MOEs) for traffic flow op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18008","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.18008/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2504.18008","created_at":"2026-07-05T11:03:23.631988+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.18008v2","created_at":"2026-07-05T11:03:23.631988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18008","created_at":"2026-07-05T11:03:23.631988+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SDD4FDINJHI","created_at":"2026-07-05T11:03:23.631988+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SDD4FDINJHIOMKN","created_at":"2026-07-05T11:03:23.631988+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SDD4FDI","created_at":"2026-07-05T11:03:23.631988+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/6SDD4FDINJHIOMKNUA5Q3CF2WL","json":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL.json","graph_json":"https://pith.science/api/pith-number/6SDD4FDINJHIOMKNUA5Q3CF2WL/graph.json","events_json":"https://pith.science/api/pith-number/6SDD4FDINJHIOMKNUA5Q3CF2WL/events.json","paper":"https://pith.science/paper/6SDD4FDI"},"agent_actions":{"view_html":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL","download_json":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL.json","view_paper":"https://pith.science/paper/6SDD4FDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.18008&json=true","fetch_graph":"https://pith.science/api/pith-number/6SDD4FDINJHIOMKNUA5Q3CF2WL/graph.json","fetch_events":"https://pith.science/api/pith-number/6SDD4FDINJHIOMKNUA5Q3CF2WL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL/action/storage_attestation","attest_author":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL/action/author_attestation","sign_citation":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL/action/citation_signature","submit_replication":"https://pith.science/pith/6SDD4FDINJHIOMKNUA5Q3CF2WL/action/replication_record"}},"created_at":"2026-07-05T11:03:23.631988+00:00","updated_at":"2026-07-05T11:03:23.631988+00:00"}