{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:D5KDX4P2EIYFQUSVLTCILU4PUZ","short_pith_number":"pith:D5KDX4P2","schema_version":"1.0","canonical_sha256":"1f543bf1fa22305852555cc485d38fa66a37d0a7a224c05a371a7d14963bf292","source":{"kind":"arxiv","id":"1312.1873","version":1},"attestation_state":"computed","paper":{"title":"Travel time estimation for ambulances using Bayesian data augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Bradford S. Westgate, David S. Matteson, Dawn B. Woodard, Shane G. Henderson","submitted_at":"2013-12-06T14:33:25Z","abstract_excerpt":"We introduce a Bayesian model for estimating the distribution of ambulance travel times on each road segment in a city, using Global Positioning System (GPS) data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. We simultaneously estimate the paths, travel times, and parameters of each road segment travel time distribution using Bayesian data augmentation. To draw ambulance path samples, we use a novel reversible jump Metropolis-Hastings step. We also introduce two simpler estimation methods based on GPS speed data. We c"},"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":"1312.1873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-12-06T14:33:25Z","cross_cats_sorted":[],"title_canon_sha256":"b7753c5618069f83a15ef62c9a843dbc756bfde0539c49bf779a29fd3a9e1602","abstract_canon_sha256":"bb60aa7fb0e799caa5d0160a8f8f86effc7938926c80e21aa4229d3d11a2b3b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:05:19.560802Z","signature_b64":"AwNw37+31uOpcP7b9/gpFasyTSrxqgHc7n3SZ07ONHSpXa4WsTon3RKQGFbTB72TJSS3oNKQ3FjIagMJB0YWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f543bf1fa22305852555cc485d38fa66a37d0a7a224c05a371a7d14963bf292","last_reissued_at":"2026-05-18T03:05:19.560287Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:05:19.560287Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Travel time estimation for ambulances using Bayesian data augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Bradford S. Westgate, David S. Matteson, Dawn B. Woodard, Shane G. Henderson","submitted_at":"2013-12-06T14:33:25Z","abstract_excerpt":"We introduce a Bayesian model for estimating the distribution of ambulance travel times on each road segment in a city, using Global Positioning System (GPS) data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. We simultaneously estimate the paths, travel times, and parameters of each road segment travel time distribution using Bayesian data augmentation. To draw ambulance path samples, we use a novel reversible jump Metropolis-Hastings step. We also introduce two simpler estimation methods based on GPS speed data. We c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.1873","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":"1312.1873","created_at":"2026-05-18T03:05:19.560385+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.1873v1","created_at":"2026-05-18T03:05:19.560385+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.1873","created_at":"2026-05-18T03:05:19.560385+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5KDX4P2EIYF","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5KDX4P2EIYFQUSV","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5KDX4P2","created_at":"2026-05-18T12:27:40.988391+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/D5KDX4P2EIYFQUSVLTCILU4PUZ","json":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ.json","graph_json":"https://pith.science/api/pith-number/D5KDX4P2EIYFQUSVLTCILU4PUZ/graph.json","events_json":"https://pith.science/api/pith-number/D5KDX4P2EIYFQUSVLTCILU4PUZ/events.json","paper":"https://pith.science/paper/D5KDX4P2"},"agent_actions":{"view_html":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ","download_json":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ.json","view_paper":"https://pith.science/paper/D5KDX4P2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.1873&json=true","fetch_graph":"https://pith.science/api/pith-number/D5KDX4P2EIYFQUSVLTCILU4PUZ/graph.json","fetch_events":"https://pith.science/api/pith-number/D5KDX4P2EIYFQUSVLTCILU4PUZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ/action/storage_attestation","attest_author":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ/action/author_attestation","sign_citation":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ/action/citation_signature","submit_replication":"https://pith.science/pith/D5KDX4P2EIYFQUSVLTCILU4PUZ/action/replication_record"}},"created_at":"2026-05-18T03:05:19.560385+00:00","updated_at":"2026-05-18T03:05:19.560385+00:00"}