{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UUZZH5S2OXEPHC5MCHFYWBB4D2","short_pith_number":"pith:UUZZH5S2","schema_version":"1.0","canonical_sha256":"a53393f65a75c8f38bac11cb8b043c1e9537bb10f0f2de1ed6366fe151917b1b","source":{"kind":"arxiv","id":"1710.04350","version":1},"attestation_state":"computed","paper":{"title":"A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ishan Jindal, Jieping Ye, Matthew Nokleby, Tony (Zhiwei) Qin, Xuewen Chen","submitted_at":"2017-10-12T03:21:16Z","abstract_excerpt":"In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of "},"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":"1710.04350","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-12T03:21:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c45b6cf7bebe8e83d1c16023255c240528b9c823fcb381d2531ca460570328b3","abstract_canon_sha256":"a436cc3a76d50ef9ef5c9ef796b1ba3110b61b672351c95ee02330f04ea6f824"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:01.299837Z","signature_b64":"m/qzrrzLFJpl9bNmGZEB9uctAwGyMcnvbffGqHVaWkdACjU97dsTojKVouHdXv6dKB6emqNbDO+beHucA6fBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a53393f65a75c8f38bac11cb8b043c1e9537bb10f0f2de1ed6366fe151917b1b","last_reissued_at":"2026-05-18T00:33:01.299099Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:01.299099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ishan Jindal, Jieping Ye, Matthew Nokleby, Tony (Zhiwei) Qin, Xuewen Chen","submitted_at":"2017-10-12T03:21:16Z","abstract_excerpt":"In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04350","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":"1710.04350","created_at":"2026-05-18T00:33:01.299224+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04350v1","created_at":"2026-05-18T00:33:01.299224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04350","created_at":"2026-05-18T00:33:01.299224+00:00"},{"alias_kind":"pith_short_12","alias_value":"UUZZH5S2OXEP","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"UUZZH5S2OXEPHC5M","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"UUZZH5S2","created_at":"2026-05-18T12:31:49.984773+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/UUZZH5S2OXEPHC5MCHFYWBB4D2","json":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2.json","graph_json":"https://pith.science/api/pith-number/UUZZH5S2OXEPHC5MCHFYWBB4D2/graph.json","events_json":"https://pith.science/api/pith-number/UUZZH5S2OXEPHC5MCHFYWBB4D2/events.json","paper":"https://pith.science/paper/UUZZH5S2"},"agent_actions":{"view_html":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2","download_json":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2.json","view_paper":"https://pith.science/paper/UUZZH5S2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04350&json=true","fetch_graph":"https://pith.science/api/pith-number/UUZZH5S2OXEPHC5MCHFYWBB4D2/graph.json","fetch_events":"https://pith.science/api/pith-number/UUZZH5S2OXEPHC5MCHFYWBB4D2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2/action/storage_attestation","attest_author":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2/action/author_attestation","sign_citation":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2/action/citation_signature","submit_replication":"https://pith.science/pith/UUZZH5S2OXEPHC5MCHFYWBB4D2/action/replication_record"}},"created_at":"2026-05-18T00:33:01.299224+00:00","updated_at":"2026-05-18T00:33:01.299224+00:00"}