{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VTJZ5DWO2NEAGVKETMPKBNUD5X","short_pith_number":"pith:VTJZ5DWO","schema_version":"1.0","canonical_sha256":"acd39e8eced3480355449b1ea0b683ede1382a4dce4e521e8f7b96ad363f220b","source":{"kind":"arxiv","id":"1602.04301","version":3},"attestation_state":"computed","paper":{"title":"Latent Space Model for Road Networks to Predict Time-Varying Traffic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.SI","authors_text":"Cyrus Shahabi, Dingxiong Deng, Linhong Zhu, Rose Yu, Ugur Demiryurek, Yan Liu","submitted_at":"2016-02-13T08:18:07Z","abstract_excerpt":"Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are"},"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":"1602.04301","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-02-13T08:18:07Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"ddf92b0f229c8a73ea15de931b95d49472233aef1e8db96cf1afa41cbc3ceadd","abstract_canon_sha256":"5142dbd4a3f2115c7a5cef3b9c4765be1bca995384637f08d1950cc6726b34ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:42.820812Z","signature_b64":"5dKO1atX0TkR18eacfgBcq97H8l1zk11hwSYEYiNIJYpRPxObhjR7U8ULO8xseGLCkP7Rs2qYcALa5QMOXblDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acd39e8eced3480355449b1ea0b683ede1382a4dce4e521e8f7b96ad363f220b","last_reissued_at":"2026-05-18T01:10:42.820290Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:42.820290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent Space Model for Road Networks to Predict Time-Varying Traffic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.SI","authors_text":"Cyrus Shahabi, Dingxiong Deng, Linhong Zhu, Rose Yu, Ugur Demiryurek, Yan Liu","submitted_at":"2016-02-13T08:18:07Z","abstract_excerpt":"Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04301","kind":"arxiv","version":3},"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":"1602.04301","created_at":"2026-05-18T01:10:42.820377+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.04301v3","created_at":"2026-05-18T01:10:42.820377+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04301","created_at":"2026-05-18T01:10:42.820377+00:00"},{"alias_kind":"pith_short_12","alias_value":"VTJZ5DWO2NEA","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VTJZ5DWO2NEAGVKE","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VTJZ5DWO","created_at":"2026-05-18T12:30:48.956258+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/VTJZ5DWO2NEAGVKETMPKBNUD5X","json":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X.json","graph_json":"https://pith.science/api/pith-number/VTJZ5DWO2NEAGVKETMPKBNUD5X/graph.json","events_json":"https://pith.science/api/pith-number/VTJZ5DWO2NEAGVKETMPKBNUD5X/events.json","paper":"https://pith.science/paper/VTJZ5DWO"},"agent_actions":{"view_html":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X","download_json":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X.json","view_paper":"https://pith.science/paper/VTJZ5DWO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.04301&json=true","fetch_graph":"https://pith.science/api/pith-number/VTJZ5DWO2NEAGVKETMPKBNUD5X/graph.json","fetch_events":"https://pith.science/api/pith-number/VTJZ5DWO2NEAGVKETMPKBNUD5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X/action/storage_attestation","attest_author":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X/action/author_attestation","sign_citation":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X/action/citation_signature","submit_replication":"https://pith.science/pith/VTJZ5DWO2NEAGVKETMPKBNUD5X/action/replication_record"}},"created_at":"2026-05-18T01:10:42.820377+00:00","updated_at":"2026-05-18T01:10:42.820377+00:00"}