{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QBCHQGCKNGVXFTYAXMVM646BXP","short_pith_number":"pith:QBCHQGCK","schema_version":"1.0","canonical_sha256":"804478184a69ab72cf00bb2acf73c1bbc86d56b23a3ccad124f39981039fa03d","source":{"kind":"arxiv","id":"1807.10934","version":1},"attestation_state":"computed","paper":{"title":"Bike Flow Prediction with Multi-Graph Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Di Chai, Leye Wang, Qiang Yang","submitted_at":"2018-07-28T13:35:37Z","abstract_excerpt":"One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained bike sharing system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy leveraging the breakthroughs of deep learning techniques. We propose a new multi-graph convolutional ne"},"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":"1807.10934","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-28T13:35:37Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"5c57f8cd4be74413581dce3220643c6ab29c298abcf5fec979270e042c8890b9","abstract_canon_sha256":"45b75204d943e36d086970971d9d930bff2f89e499185a0dd0a8f8fcbd41dc9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:34.966843Z","signature_b64":"rqOPWo8hQJby/oDsDJdQVwQRdRKOWjglbRt4QB3dV5maRvHRS24jT55sZN1p+o+LwXQxuG7FXzJoEKxA9jVnAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"804478184a69ab72cf00bb2acf73c1bbc86d56b23a3ccad124f39981039fa03d","last_reissued_at":"2026-05-18T00:09:34.966439Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:34.966439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bike Flow Prediction with Multi-Graph Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Di Chai, Leye Wang, Qiang Yang","submitted_at":"2018-07-28T13:35:37Z","abstract_excerpt":"One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained bike sharing system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy leveraging the breakthroughs of deep learning techniques. We propose a new multi-graph convolutional ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10934","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":"1807.10934","created_at":"2026-05-18T00:09:34.966504+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.10934v1","created_at":"2026-05-18T00:09:34.966504+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10934","created_at":"2026-05-18T00:09:34.966504+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBCHQGCKNGVX","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBCHQGCKNGVXFTYA","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBCHQGCK","created_at":"2026-05-18T12:32:46.962924+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/QBCHQGCKNGVXFTYAXMVM646BXP","json":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP.json","graph_json":"https://pith.science/api/pith-number/QBCHQGCKNGVXFTYAXMVM646BXP/graph.json","events_json":"https://pith.science/api/pith-number/QBCHQGCKNGVXFTYAXMVM646BXP/events.json","paper":"https://pith.science/paper/QBCHQGCK"},"agent_actions":{"view_html":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP","download_json":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP.json","view_paper":"https://pith.science/paper/QBCHQGCK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.10934&json=true","fetch_graph":"https://pith.science/api/pith-number/QBCHQGCKNGVXFTYAXMVM646BXP/graph.json","fetch_events":"https://pith.science/api/pith-number/QBCHQGCKNGVXFTYAXMVM646BXP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP/action/storage_attestation","attest_author":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP/action/author_attestation","sign_citation":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP/action/citation_signature","submit_replication":"https://pith.science/pith/QBCHQGCKNGVXFTYAXMVM646BXP/action/replication_record"}},"created_at":"2026-05-18T00:09:34.966504+00:00","updated_at":"2026-05-18T00:09:34.966504+00:00"}