{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IXVU2ZBUKJIQDKG2BIOJ7JCBOD","short_pith_number":"pith:IXVU2ZBU","canonical_record":{"source":{"id":"1912.06991","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-15T06:49:43Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"8368aa26ad7d87dc40d3f7c4d6421321987dd2c0876cb63bf97a5a83cd35f478","abstract_canon_sha256":"f72369c69a4cbcb5aa9ef3a82a44610a8ee7c1ea461eb47919c58d8d1ec6212f"},"schema_version":"1.0"},"canonical_sha256":"45eb4d6434525101a8da0a1c9fa44170f18c1490f0de890d08899aba832481fb","source":{"kind":"arxiv","id":"1912.06991","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.06991","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"arxiv_version","alias_value":"1912.06991v2","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.06991","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_12","alias_value":"IXVU2ZBUKJIQ","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_16","alias_value":"IXVU2ZBUKJIQDKG2","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_8","alias_value":"IXVU2ZBU","created_at":"2026-07-05T00:27:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IXVU2ZBUKJIQDKG2BIOJ7JCBOD","target":"record","payload":{"canonical_record":{"source":{"id":"1912.06991","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-15T06:49:43Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"8368aa26ad7d87dc40d3f7c4d6421321987dd2c0876cb63bf97a5a83cd35f478","abstract_canon_sha256":"f72369c69a4cbcb5aa9ef3a82a44610a8ee7c1ea461eb47919c58d8d1ec6212f"},"schema_version":"1.0"},"canonical_sha256":"45eb4d6434525101a8da0a1c9fa44170f18c1490f0de890d08899aba832481fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:46.002774Z","signature_b64":"AlOgcxm5bi53H/bRd3mPcoWxFJqYC4hhWAFr5R0Uc98r3JKkfiM+tEmLxTREWVv1BRAX9Xbu/hAVByhe6jtBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45eb4d6434525101a8da0a1c9fa44170f18c1490f0de890d08899aba832481fb","last_reissued_at":"2026-07-05T00:27:46.002294Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:46.002294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.06991","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:27:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DYq8lavdECsK75BL1tFmfclZDig0TyF3Vs9mMB6uCijmPYY4YPurrsn2Xtxwq3exhpg62DqIyA3spydDLXstCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T13:38:01.401930Z"},"content_sha256":"ef5197b7b5c2300e8ff0c0f1f1ac07336018be32789217440cee67a4cb84de93","schema_version":"1.0","event_id":"sha256:ef5197b7b5c2300e8ff0c0f1f1ac07336018be32789217440cee67a4cb84de93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IXVU2ZBUKJIQDKG2BIOJ7JCBOD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"stat.ML","authors_text":"Abolfazl Mohammadian, Amir Bahador Parsa, Homa Taghipour, Rishabh Singh Chauhan, Sybil Derrible","submitted_at":"2019-12-15T06:49:43Z","abstract_excerpt":"Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicago. These two techniques are selected because they are known to perform well with sequential data (i.e., time series). The full dataset consists of 241 accident and 6,038 non-accident cases selected from Chicago expressway, and it includes traffic spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.06991","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/1912.06991/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":"2026-07-05T00:27:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dVc0JvkOUeq7waJ5Qy30hK+iRbmpj5/bkYi5Kb2h7uI+vuH2ZRIX3x+/qoKUtbDc7LbR47Dsg2DkvIU1SlR0AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T13:38:01.402476Z"},"content_sha256":"6843315afa246240426f50bf3ecac27006c184ea4ac4bf7240b78a282a474bf4","schema_version":"1.0","event_id":"sha256:6843315afa246240426f50bf3ecac27006c184ea4ac4bf7240b78a282a474bf4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/bundle.json","state_url":"https://pith.science/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/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":"2026-08-22T13:38:01Z","links":{"resolver":"https://pith.science/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD","bundle":"https://pith.science/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/bundle.json","state":"https://pith.science/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IXVU2ZBUKJIQDKG2BIOJ7JCBOD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IXVU2ZBUKJIQDKG2BIOJ7JCBOD","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":"f72369c69a4cbcb5aa9ef3a82a44610a8ee7c1ea461eb47919c58d8d1ec6212f","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-15T06:49:43Z","title_canon_sha256":"8368aa26ad7d87dc40d3f7c4d6421321987dd2c0876cb63bf97a5a83cd35f478"},"schema_version":"1.0","source":{"id":"1912.06991","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.06991","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"arxiv_version","alias_value":"1912.06991v2","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.06991","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_12","alias_value":"IXVU2ZBUKJIQ","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_16","alias_value":"IXVU2ZBUKJIQDKG2","created_at":"2026-07-05T00:27:46Z"},{"alias_kind":"pith_short_8","alias_value":"IXVU2ZBU","created_at":"2026-07-05T00:27:46Z"}],"graph_snapshots":[{"event_id":"sha256:6843315afa246240426f50bf3ecac27006c184ea4ac4bf7240b78a282a474bf4","target":"graph","created_at":"2026-07-05T00:27:46Z","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/1912.06991/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicago. These two techniques are selected because they are known to perform well with sequential data (i.e., time series). The full dataset consists of 241 accident and 6,038 non-accident cases selected from Chicago expressway, and it includes traffic spa","authors_text":"Abolfazl Mohammadian, Amir Bahador Parsa, Homa Taghipour, Rishabh Singh Chauhan, Sybil Derrible","cross_cats":["cs.LG","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-15T06:49:43Z","title":"Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.06991","kind":"arxiv","version":2},"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:ef5197b7b5c2300e8ff0c0f1f1ac07336018be32789217440cee67a4cb84de93","target":"record","created_at":"2026-07-05T00:27:46Z","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":"f72369c69a4cbcb5aa9ef3a82a44610a8ee7c1ea461eb47919c58d8d1ec6212f","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-15T06:49:43Z","title_canon_sha256":"8368aa26ad7d87dc40d3f7c4d6421321987dd2c0876cb63bf97a5a83cd35f478"},"schema_version":"1.0","source":{"id":"1912.06991","kind":"arxiv","version":2}},"canonical_sha256":"45eb4d6434525101a8da0a1c9fa44170f18c1490f0de890d08899aba832481fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45eb4d6434525101a8da0a1c9fa44170f18c1490f0de890d08899aba832481fb","first_computed_at":"2026-07-05T00:27:46.002294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:27:46.002294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AlOgcxm5bi53H/bRd3mPcoWxFJqYC4hhWAFr5R0Uc98r3JKkfiM+tEmLxTREWVv1BRAX9Xbu/hAVByhe6jtBDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:27:46.002774Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.06991","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef5197b7b5c2300e8ff0c0f1f1ac07336018be32789217440cee67a4cb84de93","sha256:6843315afa246240426f50bf3ecac27006c184ea4ac4bf7240b78a282a474bf4"],"state_sha256":"d74ca1983673de854831dd8f41fc16dd29d2adf85782443a7f96f688f05eadeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WdbWJBvbuy4BPMmCCWnI/ADy00WCzlCX3b4mSvh4ZfNns3zHJizDdNQTv1pSM7d07Qbx4EYeWX1G122QZdYVBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T13:38:01.407640Z","bundle_sha256":"e20e903f475d5e8b2b61c868afadf05ab375e38d3000b8993585de1979b88afe"}}