{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:T6DKUJGK4JDWLCGIO7JRZGZY7I","short_pith_number":"pith:T6DKUJGK","schema_version":"1.0","canonical_sha256":"9f86aa24cae2476588c877d31c9b38fa3390b2d0a4a4b708d402df5bc3bd3cd2","source":{"kind":"arxiv","id":"2108.09091","version":1},"attestation_state":"computed","paper":{"title":"DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Du Yin, Hangchen Liu, Jiewen Deng, Jinliang Deng, Renhe Jiang, Ryosuke Shibasaki, Xuan Song, Yizhuo Wang, Zekun Cai, Zhaonan Wang","submitted_at":"2021-08-20T10:08:26Z","abstract_excerpt":"Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the 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":"2108.09091","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-20T10:08:26Z","cross_cats_sorted":[],"title_canon_sha256":"3f77f4c59a4adf46d8a2ef0f96df2976a85f80cbe8301e43dcef7639f361d5e2","abstract_canon_sha256":"d5b77a2b4c4412799bc8001ee065e2d0d2ada47f55cb5fd46fbf01550aca5f93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:33.983111Z","signature_b64":"AnhK4/1wKzhiQdPJb5exzlCdDd75BQHa+xB3H2xbC0HZMzF6HlyBu8UGoeF4noCA3L41KIeAbLHVBxN5ADfgCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f86aa24cae2476588c877d31c9b38fa3390b2d0a4a4b708d402df5bc3bd3cd2","last_reissued_at":"2026-07-05T03:07:33.982735Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:33.982735Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Du Yin, Hangchen Liu, Jiewen Deng, Jinliang Deng, Renhe Jiang, Ryosuke Shibasaki, Xuan Song, Yizhuo Wang, Zekun Cai, Zhaonan Wang","submitted_at":"2021-08-20T10:08:26Z","abstract_excerpt":"Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.09091","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.09091/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":"2108.09091","created_at":"2026-07-05T03:07:33.982788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.09091v1","created_at":"2026-07-05T03:07:33.982788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.09091","created_at":"2026-07-05T03:07:33.982788+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6DKUJGK4JDW","created_at":"2026-07-05T03:07:33.982788+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6DKUJGK4JDWLCGI","created_at":"2026-07-05T03:07:33.982788+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6DKUJGK","created_at":"2026-07-05T03:07:33.982788+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/T6DKUJGK4JDWLCGIO7JRZGZY7I","json":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I.json","graph_json":"https://pith.science/api/pith-number/T6DKUJGK4JDWLCGIO7JRZGZY7I/graph.json","events_json":"https://pith.science/api/pith-number/T6DKUJGK4JDWLCGIO7JRZGZY7I/events.json","paper":"https://pith.science/paper/T6DKUJGK"},"agent_actions":{"view_html":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I","download_json":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I.json","view_paper":"https://pith.science/paper/T6DKUJGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.09091&json=true","fetch_graph":"https://pith.science/api/pith-number/T6DKUJGK4JDWLCGIO7JRZGZY7I/graph.json","fetch_events":"https://pith.science/api/pith-number/T6DKUJGK4JDWLCGIO7JRZGZY7I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I/action/storage_attestation","attest_author":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I/action/author_attestation","sign_citation":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I/action/citation_signature","submit_replication":"https://pith.science/pith/T6DKUJGK4JDWLCGIO7JRZGZY7I/action/replication_record"}},"created_at":"2026-07-05T03:07:33.982788+00:00","updated_at":"2026-07-05T03:07:33.982788+00:00"}