{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:JLEARO6JHPESYVJSQ3IG5EDP5F","short_pith_number":"pith:JLEARO6J","canonical_record":{"source":{"id":"2509.02614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-08-31T04:13:32Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"7111507ba5251424e166573be028bf6fd4b56f9c6b305c3615b58b9bbfdea7be","abstract_canon_sha256":"aa693d6f4019b55e206ef540e3be9dc01dc809c7bbf52581c87654bf2e66d4c8"},"schema_version":"1.0"},"canonical_sha256":"4ac808bbc93bc92c553286d06e906fe9722d47aeb0c770129ea85fb93dbd5cb9","source":{"kind":"arxiv","id":"2509.02614","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.02614","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"arxiv_version","alias_value":"2509.02614v1","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02614","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_12","alias_value":"JLEARO6JHPES","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_16","alias_value":"JLEARO6JHPESYVJS","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_8","alias_value":"JLEARO6J","created_at":"2026-07-05T12:03:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:JLEARO6JHPESYVJSQ3IG5EDP5F","target":"record","payload":{"canonical_record":{"source":{"id":"2509.02614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-08-31T04:13:32Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"7111507ba5251424e166573be028bf6fd4b56f9c6b305c3615b58b9bbfdea7be","abstract_canon_sha256":"aa693d6f4019b55e206ef540e3be9dc01dc809c7bbf52581c87654bf2e66d4c8"},"schema_version":"1.0"},"canonical_sha256":"4ac808bbc93bc92c553286d06e906fe9722d47aeb0c770129ea85fb93dbd5cb9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:54.389821Z","signature_b64":"MmZvJJIAHUW5GFIMiM0TN18Xy5lUfnQL13h8U09pkGimPEuOyWM+B/oxH5f1/S6b+ryox5mmdJnSpy7o/UrBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ac808bbc93bc92c553286d06e906fe9722d47aeb0c770129ea85fb93dbd5cb9","last_reissued_at":"2026-07-05T12:03:54.389224Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:54.389224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.02614","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":"2026-07-05T12:03:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6i5qpHjEExMX6noGVRFvxxHP5N5oAMdjteOj0X7P1owLGtgZOgu+k1dV3ZArgq14jM6In52D8gZ5acFcO9H5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T03:12:32.925540Z"},"content_sha256":"2f3ab9a64e7974c2ad4827b1d74a46ac14a2fc03f593e43966261fed1ef130ae","schema_version":"1.0","event_id":"sha256:2f3ab9a64e7974c2ad4827b1d74a46ac14a2fc03f593e43966261fed1ef130ae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:JLEARO6JHPESYVJSQ3IG5EDP5F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"stat.AP","authors_text":"Lishuai Li, Montserrat Guillen, Xinbo Zhang, Xin Li, Youhua Frank Chen","submitted_at":"2025-08-31T04:13:32Z","abstract_excerpt":"Driving behavior big data leverages multi-sensor telematics to understand how people drive and powers applications such as risk evaluation, insurance pricing, and targeted intervention. Usage-based insurance (UBI) built on these data has become mainstream. Telematics-captured near-miss events (NMEs) provide a timely alternative to claim-based risk, but weekly NMEs are sparse, highly zero-inflated, and behaviorally heterogeneous even after exposure normalization. Analyzing multi-sensor telematics and ADAS warnings, we show that the traditional statistical models underfit the dataset. We address"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02614","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/2509.02614/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":"2026-07-05T12:03:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t/VcM5ymcpp7r5O7hqwwa8pO/DpyjzABn5grxP4Y5NlFZ7qD4kE4jy5VeNQ1lUze4Z7zulStSwRZ89iMeY1CBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T03:12:32.925926Z"},"content_sha256":"ce759708f49486fdf958b7e9a1836b25b7c1fad90c7ae4fb555e9aea4db3e77e","schema_version":"1.0","event_id":"sha256:ce759708f49486fdf958b7e9a1836b25b7c1fad90c7ae4fb555e9aea4db3e77e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/bundle.json","state_url":"https://pith.science/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/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":"2026-07-10T03:12:32Z","links":{"resolver":"https://pith.science/pith/JLEARO6JHPESYVJSQ3IG5EDP5F","bundle":"https://pith.science/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/bundle.json","state":"https://pith.science/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JLEARO6JHPESYVJSQ3IG5EDP5F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JLEARO6JHPESYVJSQ3IG5EDP5F","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":"aa693d6f4019b55e206ef540e3be9dc01dc809c7bbf52581c87654bf2e66d4c8","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-08-31T04:13:32Z","title_canon_sha256":"7111507ba5251424e166573be028bf6fd4b56f9c6b305c3615b58b9bbfdea7be"},"schema_version":"1.0","source":{"id":"2509.02614","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.02614","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"arxiv_version","alias_value":"2509.02614v1","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02614","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_12","alias_value":"JLEARO6JHPES","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_16","alias_value":"JLEARO6JHPESYVJS","created_at":"2026-07-05T12:03:54Z"},{"alias_kind":"pith_short_8","alias_value":"JLEARO6J","created_at":"2026-07-05T12:03:54Z"}],"graph_snapshots":[{"event_id":"sha256:ce759708f49486fdf958b7e9a1836b25b7c1fad90c7ae4fb555e9aea4db3e77e","target":"graph","created_at":"2026-07-05T12:03:54Z","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/2509.02614/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Driving behavior big data leverages multi-sensor telematics to understand how people drive and powers applications such as risk evaluation, insurance pricing, and targeted intervention. Usage-based insurance (UBI) built on these data has become mainstream. Telematics-captured near-miss events (NMEs) provide a timely alternative to claim-based risk, but weekly NMEs are sparse, highly zero-inflated, and behaviorally heterogeneous even after exposure normalization. Analyzing multi-sensor telematics and ADAS warnings, we show that the traditional statistical models underfit the dataset. We address","authors_text":"Lishuai Li, Montserrat Guillen, Xinbo Zhang, Xin Li, Youhua Frank Chen","cross_cats":["cs.CE","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-08-31T04:13:32Z","title":"Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02614","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":"sha256:2f3ab9a64e7974c2ad4827b1d74a46ac14a2fc03f593e43966261fed1ef130ae","target":"record","created_at":"2026-07-05T12:03:54Z","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":"aa693d6f4019b55e206ef540e3be9dc01dc809c7bbf52581c87654bf2e66d4c8","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-08-31T04:13:32Z","title_canon_sha256":"7111507ba5251424e166573be028bf6fd4b56f9c6b305c3615b58b9bbfdea7be"},"schema_version":"1.0","source":{"id":"2509.02614","kind":"arxiv","version":1}},"canonical_sha256":"4ac808bbc93bc92c553286d06e906fe9722d47aeb0c770129ea85fb93dbd5cb9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ac808bbc93bc92c553286d06e906fe9722d47aeb0c770129ea85fb93dbd5cb9","first_computed_at":"2026-07-05T12:03:54.389224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:03:54.389224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MmZvJJIAHUW5GFIMiM0TN18Xy5lUfnQL13h8U09pkGimPEuOyWM+B/oxH5f1/S6b+ryox5mmdJnSpy7o/UrBCg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:03:54.389821Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.02614","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f3ab9a64e7974c2ad4827b1d74a46ac14a2fc03f593e43966261fed1ef130ae","sha256:ce759708f49486fdf958b7e9a1836b25b7c1fad90c7ae4fb555e9aea4db3e77e"],"state_sha256":"4b0706b6599dd5f4d9b9725632843a73719705eeed2d4431343531b1565ffe9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l5we64Yfc41dJMhOUc4aoZfmZsc7Ry7EsfKsCRvErYfXKz+0c6hmjAB/elgxakYh3dnYQwBf2YLoxZCOd5vuCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T03:12:32.927872Z","bundle_sha256":"b099de6266df5a06fa3b93795dd7bf31d12090a6b12ec8ff43ee69be25c27698"}}