{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2EZ4S3J7FTIQGLR452I56HKQ5I","short_pith_number":"pith:2EZ4S3J7","canonical_record":{"source":{"id":"2408.12890","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-23T07:51:10Z","cross_cats_sorted":[],"title_canon_sha256":"4c5a31194828ccc14da0b929ac141dec5f4fb1cbfc6a14f684a845d894b0982d","abstract_canon_sha256":"f957846f3bd1e2c893dad7049c0c47c4133f727dae9ef717e379c87e61c342c3"},"schema_version":"1.0"},"canonical_sha256":"d133c96d3f2cd1032e3cee91df1d50ea1a17a20832cbc1321fcc444420f040c5","source":{"kind":"arxiv","id":"2408.12890","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.12890","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"2408.12890v1","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.12890","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"2EZ4S3J7FTIQ","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_16","alias_value":"2EZ4S3J7FTIQGLR4","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_8","alias_value":"2EZ4S3J7","created_at":"2026-07-05T08:58:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2EZ4S3J7FTIQGLR452I56HKQ5I","target":"record","payload":{"canonical_record":{"source":{"id":"2408.12890","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-23T07:51:10Z","cross_cats_sorted":[],"title_canon_sha256":"4c5a31194828ccc14da0b929ac141dec5f4fb1cbfc6a14f684a845d894b0982d","abstract_canon_sha256":"f957846f3bd1e2c893dad7049c0c47c4133f727dae9ef717e379c87e61c342c3"},"schema_version":"1.0"},"canonical_sha256":"d133c96d3f2cd1032e3cee91df1d50ea1a17a20832cbc1321fcc444420f040c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:32.100193Z","signature_b64":"/snXjursDrpG3HV97PuBWy2WTNnas2zYM+d/8lxyC+Hpt/ngNJCNKtP52vUsrf76Kg7sB3+8pPdgA75h6DHjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d133c96d3f2cd1032e3cee91df1d50ea1a17a20832cbc1321fcc444420f040c5","last_reissued_at":"2026-07-05T08:58:32.099704Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:32.099704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.12890","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RRTqIDgRbVXZBle7ugwRpFLeJRG8oiyV1MotSvPFBrkFurLt8ogh7pS92oPZf1gzJjup0XWXB657nAhKEqyrAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T03:07:22.503083Z"},"content_sha256":"305e07bc5d8ad2afc43b0605df45c5d6d36261932d0af123eaee16972637b133","schema_version":"1.0","event_id":"sha256:305e07bc5d8ad2afc43b0605df45c5d6d36261932d0af123eaee16972637b133"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2EZ4S3J7FTIQGLR452I56HKQ5I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiple Areal Feature Aware Transportation Demand Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dongman Lee, Jisun An, Kitae Jang, Suji Kim, Sumin Han, Youngjun Park","submitted_at":"2024-08-23T07:51:10Z","abstract_excerpt":"A reliable short-term transportation demand prediction supports the authorities in improving the capability of systems by optimizing schedules, adjusting fleet sizes, and generating new transit networks. A handful of research efforts incorporate one or a few areal features while learning spatio-temporal correlation, to capture similar demand patterns between similar areas. However, urban characteristics are polymorphic, and they need to be understood by multiple areal features such as land use, sociodemographics, and place-of-interest (POI) distribution. In this paper, we propose a novel spati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.12890","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/2408.12890/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:58:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uBg0Yso1xHWwGfKLGRoC/HE5tGanmLpizxDeeFYpnQMLDC03kgxtCqkiI1GWIuTS8U3xfm/P2hNQPh6mpu79Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T03:07:22.505609Z"},"content_sha256":"c2cbb854c2dd41a9ccff0802c0de266dcd74470fa0eb44e81e2e5056782768ad","schema_version":"1.0","event_id":"sha256:c2cbb854c2dd41a9ccff0802c0de266dcd74470fa0eb44e81e2e5056782768ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/bundle.json","state_url":"https://pith.science/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T03:07:22Z","links":{"resolver":"https://pith.science/pith/2EZ4S3J7FTIQGLR452I56HKQ5I","bundle":"https://pith.science/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/bundle.json","state":"https://pith.science/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2EZ4S3J7FTIQGLR452I56HKQ5I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2EZ4S3J7FTIQGLR452I56HKQ5I","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f957846f3bd1e2c893dad7049c0c47c4133f727dae9ef717e379c87e61c342c3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-23T07:51:10Z","title_canon_sha256":"4c5a31194828ccc14da0b929ac141dec5f4fb1cbfc6a14f684a845d894b0982d"},"schema_version":"1.0","source":{"id":"2408.12890","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.12890","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"arxiv_version","alias_value":"2408.12890v1","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.12890","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_12","alias_value":"2EZ4S3J7FTIQ","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_16","alias_value":"2EZ4S3J7FTIQGLR4","created_at":"2026-07-05T08:58:32Z"},{"alias_kind":"pith_short_8","alias_value":"2EZ4S3J7","created_at":"2026-07-05T08:58:32Z"}],"graph_snapshots":[{"event_id":"sha256:c2cbb854c2dd41a9ccff0802c0de266dcd74470fa0eb44e81e2e5056782768ad","target":"graph","created_at":"2026-07-05T08:58:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2408.12890/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A reliable short-term transportation demand prediction supports the authorities in improving the capability of systems by optimizing schedules, adjusting fleet sizes, and generating new transit networks. A handful of research efforts incorporate one or a few areal features while learning spatio-temporal correlation, to capture similar demand patterns between similar areas. However, urban characteristics are polymorphic, and they need to be understood by multiple areal features such as land use, sociodemographics, and place-of-interest (POI) distribution. In this paper, we propose a novel spati","authors_text":"Dongman Lee, Jisun An, Kitae Jang, Suji Kim, Sumin Han, Youngjun Park","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-23T07:51:10Z","title":"Multiple Areal Feature Aware Transportation Demand Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.12890","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:305e07bc5d8ad2afc43b0605df45c5d6d36261932d0af123eaee16972637b133","target":"record","created_at":"2026-07-05T08:58:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f957846f3bd1e2c893dad7049c0c47c4133f727dae9ef717e379c87e61c342c3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-23T07:51:10Z","title_canon_sha256":"4c5a31194828ccc14da0b929ac141dec5f4fb1cbfc6a14f684a845d894b0982d"},"schema_version":"1.0","source":{"id":"2408.12890","kind":"arxiv","version":1}},"canonical_sha256":"d133c96d3f2cd1032e3cee91df1d50ea1a17a20832cbc1321fcc444420f040c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d133c96d3f2cd1032e3cee91df1d50ea1a17a20832cbc1321fcc444420f040c5","first_computed_at":"2026-07-05T08:58:32.099704Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:58:32.099704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/snXjursDrpG3HV97PuBWy2WTNnas2zYM+d/8lxyC+Hpt/ngNJCNKtP52vUsrf76Kg7sB3+8pPdgA75h6DHjAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:58:32.100193Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.12890","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:305e07bc5d8ad2afc43b0605df45c5d6d36261932d0af123eaee16972637b133","sha256:c2cbb854c2dd41a9ccff0802c0de266dcd74470fa0eb44e81e2e5056782768ad"],"state_sha256":"43d13e06f232005560c2bc99a0f0e8a3ba90b772832ee8743b2fe0e646d63670"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"klESPaUL2cyKQ+dGBzURbPQ9/w3fIjId0Y3+W+eBXLLLkkncRrscQXrJ8uyk8etQKAlW0zDzfF/oyJITnkBsDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T03:07:22.520275Z","bundle_sha256":"543ff911af14156d18553f4edb5a3864b987ae12111715d2be5e77e54709c2f5"}}