{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OAHF77SYGHICKJU6HFF3IER2Z6","short_pith_number":"pith:OAHF77SY","schema_version":"1.0","canonical_sha256":"700e5ffe5831d025269e394bb4123acf890936dbbafac85d2144720fc801db13","source":{"kind":"arxiv","id":"1812.08755","version":1},"attestation_state":"computed","paper":{"title":"A Bayesian Additive Model for Understanding Public Transport Usage in Special Events","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernardete Ribeiro, Filipe Rodrigues, Francisco C. Pereira, Stanislav S. Borysov","submitted_at":"2018-12-20T18:37:35Z","abstract_excerpt":"Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additiv"},"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":"1812.08755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-12-20T18:37:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8558a015ad9638eb678e51087af5529f350c6576f8a930407e7b03753d6ff7ff","abstract_canon_sha256":"5db37622b2ccd6ca55d55dbb5ddfdef5912916cbb9dbb08a34593c023dccac74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:49.245617Z","signature_b64":"LACGyW48SOW0baah/hlcNLr+Xu9rXMQHU+VRJERPUQfliSzVUBUWws2hN7YdJWSpQx88ymf7NlesstE+f1GACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"700e5ffe5831d025269e394bb4123acf890936dbbafac85d2144720fc801db13","last_reissued_at":"2026-05-17T23:57:49.245022Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:49.245022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bayesian Additive Model for Understanding Public Transport Usage in Special Events","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernardete Ribeiro, Filipe Rodrigues, Francisco C. Pereira, Stanislav S. Borysov","submitted_at":"2018-12-20T18:37:35Z","abstract_excerpt":"Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08755","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":"1812.08755","created_at":"2026-05-17T23:57:49.245100+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.08755v1","created_at":"2026-05-17T23:57:49.245100+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08755","created_at":"2026-05-17T23:57:49.245100+00:00"},{"alias_kind":"pith_short_12","alias_value":"OAHF77SYGHIC","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OAHF77SYGHICKJU6","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OAHF77SY","created_at":"2026-05-18T12:32:43.782077+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/OAHF77SYGHICKJU6HFF3IER2Z6","json":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6.json","graph_json":"https://pith.science/api/pith-number/OAHF77SYGHICKJU6HFF3IER2Z6/graph.json","events_json":"https://pith.science/api/pith-number/OAHF77SYGHICKJU6HFF3IER2Z6/events.json","paper":"https://pith.science/paper/OAHF77SY"},"agent_actions":{"view_html":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6","download_json":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6.json","view_paper":"https://pith.science/paper/OAHF77SY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.08755&json=true","fetch_graph":"https://pith.science/api/pith-number/OAHF77SYGHICKJU6HFF3IER2Z6/graph.json","fetch_events":"https://pith.science/api/pith-number/OAHF77SYGHICKJU6HFF3IER2Z6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6/action/storage_attestation","attest_author":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6/action/author_attestation","sign_citation":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6/action/citation_signature","submit_replication":"https://pith.science/pith/OAHF77SYGHICKJU6HFF3IER2Z6/action/replication_record"}},"created_at":"2026-05-17T23:57:49.245100+00:00","updated_at":"2026-05-17T23:57:49.245100+00:00"}