{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3J6HQ6CGJTZ5DWCCUTF2KQNNHV","short_pith_number":"pith:3J6HQ6CG","schema_version":"1.0","canonical_sha256":"da7c7878464cf3d1d842a4cba541ad3d7cfd39221b244d94e523f4a9df844fb8","source":{"kind":"arxiv","id":"2412.12201","version":2},"attestation_state":"computed","paper":{"title":"Embracing Large Language Models in Traffic Flow Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haomin Wen, Ming Zhang, Wei Ju, Xiao Luo, Yusheng Zhao, Zhiping Xiao","submitted_at":"2024-12-15T03:08:28Z","abstract_excerpt":"Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes of traffic conditions. To tackle this challenge, we propose to introduce large language models (LLMs) to help traffic flow forecasting and design a novel method name"},"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":"2412.12201","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T03:08:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5ed07ef15c04e2c6bea92f22ff58e9cd3f8473622fca2b502bd2a8e1ccad6936","abstract_canon_sha256":"9738b63f9312b508263d5697fbecc160e7dd5a1565a09f65bab85a5a9d188c55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:38.086116Z","signature_b64":"P6wDYSdkA8o4en8zffZ9+G6AmvuuocU52x+r77zgMZvOviatpGIKaIHXgM5O8wibl/apePHTOsosG39I9kk+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da7c7878464cf3d1d842a4cba541ad3d7cfd39221b244d94e523f4a9df844fb8","last_reissued_at":"2026-07-05T11:46:38.085589Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:38.085589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Embracing Large Language Models in Traffic Flow Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haomin Wen, Ming Zhang, Wei Ju, Xiao Luo, Yusheng Zhao, Zhiping Xiao","submitted_at":"2024-12-15T03:08:28Z","abstract_excerpt":"Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes of traffic conditions. To tackle this challenge, we propose to introduce large language models (LLMs) to help traffic flow forecasting and design a novel method name"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12201","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/2412.12201/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":"2412.12201","created_at":"2026-07-05T11:46:38.085654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.12201v2","created_at":"2026-07-05T11:46:38.085654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12201","created_at":"2026-07-05T11:46:38.085654+00:00"},{"alias_kind":"pith_short_12","alias_value":"3J6HQ6CGJTZ5","created_at":"2026-07-05T11:46:38.085654+00:00"},{"alias_kind":"pith_short_16","alias_value":"3J6HQ6CGJTZ5DWCC","created_at":"2026-07-05T11:46:38.085654+00:00"},{"alias_kind":"pith_short_8","alias_value":"3J6HQ6CG","created_at":"2026-07-05T11:46:38.085654+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00991","citing_title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV","json":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV.json","graph_json":"https://pith.science/api/pith-number/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/graph.json","events_json":"https://pith.science/api/pith-number/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/events.json","paper":"https://pith.science/paper/3J6HQ6CG"},"agent_actions":{"view_html":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV","download_json":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV.json","view_paper":"https://pith.science/paper/3J6HQ6CG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.12201&json=true","fetch_graph":"https://pith.science/api/pith-number/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/graph.json","fetch_events":"https://pith.science/api/pith-number/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/action/storage_attestation","attest_author":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/action/author_attestation","sign_citation":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/action/citation_signature","submit_replication":"https://pith.science/pith/3J6HQ6CGJTZ5DWCCUTF2KQNNHV/action/replication_record"}},"created_at":"2026-07-05T11:46:38.085654+00:00","updated_at":"2026-07-05T11:46:38.085654+00:00"}