{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HX3ZNODYCRZVLHLH7DAQ3SU2XA","short_pith_number":"pith:HX3ZNODY","schema_version":"1.0","canonical_sha256":"3df796b8781473559d67f8c10dca9ab8031418155f49007928c50ae32c2ffa29","source":{"kind":"arxiv","id":"1811.00740","version":1},"attestation_state":"computed","paper":{"title":"Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Bo Yang, Cailian Chen, Jianping He, Xiaoyu Wang, Yang Min, Yang Zhang","submitted_at":"2018-11-02T05:08:40Z","abstract_excerpt":"Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagatio"},"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":"1811.00740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-11-02T05:08:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"be48f1f6bb059999aad1f637b12fd5bd463655371e9c867739beedeb3bbda30f","abstract_canon_sha256":"c5e0879d7ab3c0a16ca513c94ea9271d18872518f8928a5bd981c5876c6c0e3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:42.313386Z","signature_b64":"FfIIvXi0bCWiKWOHcXwCCuqGLAzVPbkxm/FyGJLG2DtxB57MD4WzSrNYXowlzZmtP+psUN1ifwGEQdFVvlzkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3df796b8781473559d67f8c10dca9ab8031418155f49007928c50ae32c2ffa29","last_reissued_at":"2026-05-18T00:01:42.312945Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:42.312945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Bo Yang, Cailian Chen, Jianping He, Xiaoyu Wang, Yang Min, Yang Zhang","submitted_at":"2018-11-02T05:08:40Z","abstract_excerpt":"Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.00740","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":"1811.00740","created_at":"2026-05-18T00:01:42.313013+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.00740v1","created_at":"2026-05-18T00:01:42.313013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.00740","created_at":"2026-05-18T00:01:42.313013+00:00"},{"alias_kind":"pith_short_12","alias_value":"HX3ZNODYCRZV","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HX3ZNODYCRZVLHLH","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HX3ZNODY","created_at":"2026-05-18T12:32:28.185984+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/HX3ZNODYCRZVLHLH7DAQ3SU2XA","json":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA.json","graph_json":"https://pith.science/api/pith-number/HX3ZNODYCRZVLHLH7DAQ3SU2XA/graph.json","events_json":"https://pith.science/api/pith-number/HX3ZNODYCRZVLHLH7DAQ3SU2XA/events.json","paper":"https://pith.science/paper/HX3ZNODY"},"agent_actions":{"view_html":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA","download_json":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA.json","view_paper":"https://pith.science/paper/HX3ZNODY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.00740&json=true","fetch_graph":"https://pith.science/api/pith-number/HX3ZNODYCRZVLHLH7DAQ3SU2XA/graph.json","fetch_events":"https://pith.science/api/pith-number/HX3ZNODYCRZVLHLH7DAQ3SU2XA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA/action/storage_attestation","attest_author":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA/action/author_attestation","sign_citation":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA/action/citation_signature","submit_replication":"https://pith.science/pith/HX3ZNODYCRZVLHLH7DAQ3SU2XA/action/replication_record"}},"created_at":"2026-05-18T00:01:42.313013+00:00","updated_at":"2026-05-18T00:01:42.313013+00:00"}