{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VBP2ZUPTSLJE3MC36OM2V7AZWT","short_pith_number":"pith:VBP2ZUPT","schema_version":"1.0","canonical_sha256":"a85facd1f392d24db05bf399aafc19b4cdaad70ae47334e2f64fd9030e568bd9","source":{"kind":"arxiv","id":"2405.11715","version":2},"attestation_state":"computed","paper":{"title":"Semantic Trajectory Data Mining with LLM-Informed POI Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Brian Yueshuai He, Chenchen Kuai, Haoxuan Ma, Jiaqi Ma, Xishun Liao, Yifan Liu","submitted_at":"2024-05-20T01:29:45Z","abstract_excerpt":"Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the inte"},"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":"2405.11715","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-05-20T01:29:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9aa15769ddafc993cbc3236220abb1baedf17308553652687c04e70a9240f4cc","abstract_canon_sha256":"a8075a26a44fecd653d007b32bb31144b5991a2545c8dc4f493ee6b803ce781e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:03.638193Z","signature_b64":"+YtSwCgaUf2dRSGkpKJcYrN439xjeWd22EWn7Qi8F8i1p7jWH+xkk1xVjmvlYZaq1pce+cORUr/t78FfHemwDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a85facd1f392d24db05bf399aafc19b4cdaad70ae47334e2f64fd9030e568bd9","last_reissued_at":"2026-07-05T08:57:03.637647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:03.637647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Trajectory Data Mining with LLM-Informed POI Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Brian Yueshuai He, Chenchen Kuai, Haoxuan Ma, Jiaqi Ma, Xishun Liao, Yifan Liu","submitted_at":"2024-05-20T01:29:45Z","abstract_excerpt":"Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11715","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/2405.11715/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":"2405.11715","created_at":"2026-07-05T08:57:03.637713+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11715v2","created_at":"2026-07-05T08:57:03.637713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11715","created_at":"2026-07-05T08:57:03.637713+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBP2ZUPTSLJE","created_at":"2026-07-05T08:57:03.637713+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBP2ZUPTSLJE3MC3","created_at":"2026-07-05T08:57:03.637713+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBP2ZUPT","created_at":"2026-07-05T08:57:03.637713+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.19510","citing_title":"Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT","json":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT.json","graph_json":"https://pith.science/api/pith-number/VBP2ZUPTSLJE3MC36OM2V7AZWT/graph.json","events_json":"https://pith.science/api/pith-number/VBP2ZUPTSLJE3MC36OM2V7AZWT/events.json","paper":"https://pith.science/paper/VBP2ZUPT"},"agent_actions":{"view_html":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT","download_json":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT.json","view_paper":"https://pith.science/paper/VBP2ZUPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11715&json=true","fetch_graph":"https://pith.science/api/pith-number/VBP2ZUPTSLJE3MC36OM2V7AZWT/graph.json","fetch_events":"https://pith.science/api/pith-number/VBP2ZUPTSLJE3MC36OM2V7AZWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT/action/storage_attestation","attest_author":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT/action/author_attestation","sign_citation":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT/action/citation_signature","submit_replication":"https://pith.science/pith/VBP2ZUPTSLJE3MC36OM2V7AZWT/action/replication_record"}},"created_at":"2026-07-05T08:57:03.637713+00:00","updated_at":"2026-07-05T08:57:03.637713+00:00"}