{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2PY6IZV5BNJL7QTAAUF5XCEAKN","short_pith_number":"pith:2PY6IZV5","canonical_record":{"source":{"id":"2406.13968","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-20T03:22:32Z","cross_cats_sorted":[],"title_canon_sha256":"926c09313134e3d06b2c4f1a61d350ee22df4517a31d5501d1f538f5ef471ac4","abstract_canon_sha256":"0b18673d5a5e1abd517b2ccbacf21688b2c269cf6508e58d5aef0cb357bdddaf"},"schema_version":"1.0"},"canonical_sha256":"d3f1e466bd0b52bfc260050bdb8880537347cbae3ad56666afbd5fb5897b4cb2","source":{"kind":"arxiv","id":"2406.13968","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.13968","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"2406.13968v1","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13968","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"2PY6IZV5BNJL","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"2PY6IZV5BNJL7QTA","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"2PY6IZV5","created_at":"2026-07-05T08:34:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2PY6IZV5BNJL7QTAAUF5XCEAKN","target":"record","payload":{"canonical_record":{"source":{"id":"2406.13968","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-20T03:22:32Z","cross_cats_sorted":[],"title_canon_sha256":"926c09313134e3d06b2c4f1a61d350ee22df4517a31d5501d1f538f5ef471ac4","abstract_canon_sha256":"0b18673d5a5e1abd517b2ccbacf21688b2c269cf6508e58d5aef0cb357bdddaf"},"schema_version":"1.0"},"canonical_sha256":"d3f1e466bd0b52bfc260050bdb8880537347cbae3ad56666afbd5fb5897b4cb2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:37.484334Z","signature_b64":"qDzV1Hk4MFKsS80yvTAEGpLoDAe14KGSHvn0F3YHIqMXfMvyruSXuZs3AqgqB/VwLj8D59QJn7kG8aZjS5G1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3f1e466bd0b52bfc260050bdb8880537347cbae3ad56666afbd5fb5897b4cb2","last_reissued_at":"2026-07-05T08:34:37.483861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:37.483861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.13968","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":"2026-07-05T08:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0eJSMBxvIGvSz1XpLFDX5KjWuXTGujfsWHWb9mUiTs/aHyEPL4DEgNXzlJGdLo34r6B+wEfvbtYxHTrpr/wTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T03:14:57.272841Z"},"content_sha256":"cf3675ee4c09222a0da1ad927e16708cd95dc1d4176b5cbd23acb99400717808","schema_version":"1.0","event_id":"sha256:cf3675ee4c09222a0da1ad927e16708cd95dc1d4176b5cbd23acb99400717808"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2PY6IZV5BNJL7QTAAUF5XCEAKN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Noushin Behboudi, Rajiv Ramnath, Sobhan Moosavi","submitted_at":"2024-06-20T03:22:32Z","abstract_excerpt":"Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13968","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/2406.13968/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":"2026-07-05T08:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LjY/U74gcVGI7X/sswehif8zGYl2ykwtiuzc3XIyYtbI2yo7KnCOw4EOeywSC3CjcR0NGOxSnsmX0Xsn143XBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T03:14:57.273234Z"},"content_sha256":"9ae82ca8776f885b304ee5d8fe942eb146f587fae3eb61d6bee75a79ace563df","schema_version":"1.0","event_id":"sha256:9ae82ca8776f885b304ee5d8fe942eb146f587fae3eb61d6bee75a79ace563df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/bundle.json","state_url":"https://pith.science/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/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":"2026-08-21T03:14:57Z","links":{"resolver":"https://pith.science/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN","bundle":"https://pith.science/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/bundle.json","state":"https://pith.science/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2PY6IZV5BNJL7QTAAUF5XCEAKN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2PY6IZV5BNJL7QTAAUF5XCEAKN","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":"0b18673d5a5e1abd517b2ccbacf21688b2c269cf6508e58d5aef0cb357bdddaf","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-20T03:22:32Z","title_canon_sha256":"926c09313134e3d06b2c4f1a61d350ee22df4517a31d5501d1f538f5ef471ac4"},"schema_version":"1.0","source":{"id":"2406.13968","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.13968","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"2406.13968v1","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13968","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"2PY6IZV5BNJL","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"2PY6IZV5BNJL7QTA","created_at":"2026-07-05T08:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"2PY6IZV5","created_at":"2026-07-05T08:34:37Z"}],"graph_snapshots":[{"event_id":"sha256:9ae82ca8776f885b304ee5d8fe942eb146f587fae3eb61d6bee75a79ace563df","target":"graph","created_at":"2026-07-05T08:34:37Z","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/2406.13968/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, ","authors_text":"Noushin Behboudi, Rajiv Ramnath, Sobhan Moosavi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-20T03:22:32Z","title":"Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13968","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:cf3675ee4c09222a0da1ad927e16708cd95dc1d4176b5cbd23acb99400717808","target":"record","created_at":"2026-07-05T08:34:37Z","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":"0b18673d5a5e1abd517b2ccbacf21688b2c269cf6508e58d5aef0cb357bdddaf","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-20T03:22:32Z","title_canon_sha256":"926c09313134e3d06b2c4f1a61d350ee22df4517a31d5501d1f538f5ef471ac4"},"schema_version":"1.0","source":{"id":"2406.13968","kind":"arxiv","version":1}},"canonical_sha256":"d3f1e466bd0b52bfc260050bdb8880537347cbae3ad56666afbd5fb5897b4cb2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3f1e466bd0b52bfc260050bdb8880537347cbae3ad56666afbd5fb5897b4cb2","first_computed_at":"2026-07-05T08:34:37.483861Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:34:37.483861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qDzV1Hk4MFKsS80yvTAEGpLoDAe14KGSHvn0F3YHIqMXfMvyruSXuZs3AqgqB/VwLj8D59QJn7kG8aZjS5G1Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:34:37.484334Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.13968","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf3675ee4c09222a0da1ad927e16708cd95dc1d4176b5cbd23acb99400717808","sha256:9ae82ca8776f885b304ee5d8fe942eb146f587fae3eb61d6bee75a79ace563df"],"state_sha256":"611555ea0db2bbfea13bca83774b455855fa5d85bdc8b1382e0e41787551eeff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gbfQTsFDw86hG86HxxGBCaESDZhvyEaU5aX8zAFb5f2/2lc9CMlyvr12EOuneAtxdZRNmb7yd1zbkICPiA2ZCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T03:14:57.276573Z","bundle_sha256":"4980c077e795db566673d24f8738db307053e1106ab540b7f4361edf0b9c46c9"}}