{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SD2DWJQSWGNAXBWQ46HUOASCM3","short_pith_number":"pith:SD2DWJQS","schema_version":"1.0","canonical_sha256":"90f43b2612b19a0b86d0e78f47024266f1bc194699b195b09f4ded8528fa62b8","source":{"kind":"arxiv","id":"2303.05012","version":2},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Danlei Hu, Hanxi Fang, Lu Chen, Tianyi Li, Yunjun Gao, Ziquan Fang","submitted_at":"2023-03-09T03:28:22Z","abstract_excerpt":"Spatio-temporal trajectory analytics is at the core of smart mobility solutions, which offers unprecedented information for diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of th"},"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":"2303.05012","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2023-03-09T03:28:22Z","cross_cats_sorted":[],"title_canon_sha256":"ceff19b5391e27aff1415afae8daf37e6930d419a7d0fdeca2d5b1be025b1965","abstract_canon_sha256":"80c0d1463886930fa5dda54932130b27b86394fb927da283ca0e5f6675fe9f36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:57.978130Z","signature_b64":"iTh6Y9Jt0f/ktNn66MV8AwZm/RlJq9QA2k4FqxSore6B0IMplbecQgQ+SrW1JHePJLDyFkO8KF5l+5dvNL1WCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90f43b2612b19a0b86d0e78f47024266f1bc194699b195b09f4ded8528fa62b8","last_reissued_at":"2026-07-05T05:51:57.977657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:57.977657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Danlei Hu, Hanxi Fang, Lu Chen, Tianyi Li, Yunjun Gao, Ziquan Fang","submitted_at":"2023-03-09T03:28:22Z","abstract_excerpt":"Spatio-temporal trajectory analytics is at the core of smart mobility solutions, which offers unprecedented information for diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05012","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.05012/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2303.05012","created_at":"2026-07-05T05:51:57.977713+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05012v2","created_at":"2026-07-05T05:51:57.977713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05012","created_at":"2026-07-05T05:51:57.977713+00:00"},{"alias_kind":"pith_short_12","alias_value":"SD2DWJQSWGNA","created_at":"2026-07-05T05:51:57.977713+00:00"},{"alias_kind":"pith_short_16","alias_value":"SD2DWJQSWGNAXBWQ","created_at":"2026-07-05T05:51:57.977713+00:00"},{"alias_kind":"pith_short_8","alias_value":"SD2DWJQS","created_at":"2026-07-05T05:51:57.977713+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04902","citing_title":"AegisTS: An Agent-Driven Hierarchical Reinforcement Learning System for Multivariate Time Series Data Cleaning","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3","json":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3.json","graph_json":"https://pith.science/api/pith-number/SD2DWJQSWGNAXBWQ46HUOASCM3/graph.json","events_json":"https://pith.science/api/pith-number/SD2DWJQSWGNAXBWQ46HUOASCM3/events.json","paper":"https://pith.science/paper/SD2DWJQS"},"agent_actions":{"view_html":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3","download_json":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3.json","view_paper":"https://pith.science/paper/SD2DWJQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05012&json=true","fetch_graph":"https://pith.science/api/pith-number/SD2DWJQSWGNAXBWQ46HUOASCM3/graph.json","fetch_events":"https://pith.science/api/pith-number/SD2DWJQSWGNAXBWQ46HUOASCM3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3/action/storage_attestation","attest_author":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3/action/author_attestation","sign_citation":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3/action/citation_signature","submit_replication":"https://pith.science/pith/SD2DWJQSWGNAXBWQ46HUOASCM3/action/replication_record"}},"created_at":"2026-07-05T05:51:57.977713+00:00","updated_at":"2026-07-05T05:51:57.977713+00:00"}