{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:R7LBHOE6EEJ2EV5DQT2KRVFAZO","short_pith_number":"pith:R7LBHOE6","canonical_record":{"source":{"id":"2505.03816","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-05-02T17:41:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"50e878aab2db3b6b2675ed1fb0b02872b62b92b7c749ec80991c8af2dd8c967b","abstract_canon_sha256":"cc3fdb965983b09b84b376f8f98e6ba9329405416333115c909ebb4330077a8f"},"schema_version":"1.0"},"canonical_sha256":"8fd613b89e2113a257a384f4a8d4a0cbb44e2a1b3c154e5a967777f6bfe5bcdb","source":{"kind":"arxiv","id":"2505.03816","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03816","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03816v1","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03816","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"R7LBHOE6EEJ2","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"R7LBHOE6EEJ2EV5D","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"R7LBHOE6","created_at":"2026-07-05T10:59:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:R7LBHOE6EEJ2EV5DQT2KRVFAZO","target":"record","payload":{"canonical_record":{"source":{"id":"2505.03816","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-05-02T17:41:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"50e878aab2db3b6b2675ed1fb0b02872b62b92b7c749ec80991c8af2dd8c967b","abstract_canon_sha256":"cc3fdb965983b09b84b376f8f98e6ba9329405416333115c909ebb4330077a8f"},"schema_version":"1.0"},"canonical_sha256":"8fd613b89e2113a257a384f4a8d4a0cbb44e2a1b3c154e5a967777f6bfe5bcdb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:32.051546Z","signature_b64":"07M1Whdg1CNIzC3pXNJcG7na+M6Ye0C0Ht9W7d51v9nXXEhGhBP7iLIBPH8QREG3m3itdtalsyM8lbEhHissCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fd613b89e2113a257a384f4a8d4a0cbb44e2a1b3c154e5a967777f6bfe5bcdb","last_reissued_at":"2026-07-05T10:59:32.051042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:32.051042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.03816","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":"2026-07-05T10:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"20BuCiQoXmivWy8fwXFTfbeFPdUVsNjOTOenWCxUo5EGzwmBgyrYAjOX11y+YKq0pEGaEy+u3Kqz41aH6+gGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:31:24.173290Z"},"content_sha256":"1898b554045f43535c62f2c292d1f9424d457a03fbb4e3f24eb2668f866979b8","schema_version":"1.0","event_id":"sha256:1898b554045f43535c62f2c292d1f9424d457a03fbb4e3f24eb2668f866979b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:R7LBHOE6EEJ2EV5DQT2KRVFAZO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geospatial and Temporal Trends in Urban Transportation: A Study of NYC Taxis and Pathao Food Deliveries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SI","authors_text":"Bidyarthi Paul, Dipta Biswas, Fariha Tasnim Chowdhury, Meherin Sultana","submitted_at":"2025-05-02T17:41:17Z","abstract_excerpt":"Urban transportation plays a vital role in modern city life, affecting how efficiently people and goods move around. This study analyzes transportation patterns using two datasets: the NYC Taxi Trip dataset from New York City and the Pathao Food Trip dataset from Dhaka, Bangladesh. Our goal is to identify key trends in demand, peak times, and important geographical hotspots. We start with Exploratory Data Analysis (EDA) to understand the basic characteristics of the datasets. Next, we perform geospatial analysis to map out high-demand and low-demand regions. We use the SARIMAX model for time s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03816","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/2505.03816/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":"2026-07-05T10:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pl7FsM6k2p7FbAe7DbxqsDeuqAMBdtSjHue/BTOZgqhxngvSq9h7FS9JGDsS9dHuXvx6Fj52hFIqh/BE2gWEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:31:24.174072Z"},"content_sha256":"05464a9ff757bcf756a1f70272ce6242653245320143615d77218f62668dbff9","schema_version":"1.0","event_id":"sha256:05464a9ff757bcf756a1f70272ce6242653245320143615d77218f62668dbff9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/bundle.json","state_url":"https://pith.science/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/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":"2026-08-05T14:31:24Z","links":{"resolver":"https://pith.science/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO","bundle":"https://pith.science/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/bundle.json","state":"https://pith.science/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R7LBHOE6EEJ2EV5DQT2KRVFAZO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R7LBHOE6EEJ2EV5DQT2KRVFAZO","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":"cc3fdb965983b09b84b376f8f98e6ba9329405416333115c909ebb4330077a8f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-05-02T17:41:17Z","title_canon_sha256":"50e878aab2db3b6b2675ed1fb0b02872b62b92b7c749ec80991c8af2dd8c967b"},"schema_version":"1.0","source":{"id":"2505.03816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03816","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03816v1","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03816","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"R7LBHOE6EEJ2","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"R7LBHOE6EEJ2EV5D","created_at":"2026-07-05T10:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"R7LBHOE6","created_at":"2026-07-05T10:59:32Z"}],"graph_snapshots":[{"event_id":"sha256:05464a9ff757bcf756a1f70272ce6242653245320143615d77218f62668dbff9","target":"graph","created_at":"2026-07-05T10:59:32Z","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/2505.03816/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Urban transportation plays a vital role in modern city life, affecting how efficiently people and goods move around. This study analyzes transportation patterns using two datasets: the NYC Taxi Trip dataset from New York City and the Pathao Food Trip dataset from Dhaka, Bangladesh. Our goal is to identify key trends in demand, peak times, and important geographical hotspots. We start with Exploratory Data Analysis (EDA) to understand the basic characteristics of the datasets. Next, we perform geospatial analysis to map out high-demand and low-demand regions. We use the SARIMAX model for time s","authors_text":"Bidyarthi Paul, Dipta Biswas, Fariha Tasnim Chowdhury, Meherin Sultana","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-05-02T17:41:17Z","title":"Geospatial and Temporal Trends in Urban Transportation: A Study of NYC Taxis and Pathao Food Deliveries"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03816","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":"sha256:1898b554045f43535c62f2c292d1f9424d457a03fbb4e3f24eb2668f866979b8","target":"record","created_at":"2026-07-05T10:59:32Z","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":"cc3fdb965983b09b84b376f8f98e6ba9329405416333115c909ebb4330077a8f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-05-02T17:41:17Z","title_canon_sha256":"50e878aab2db3b6b2675ed1fb0b02872b62b92b7c749ec80991c8af2dd8c967b"},"schema_version":"1.0","source":{"id":"2505.03816","kind":"arxiv","version":1}},"canonical_sha256":"8fd613b89e2113a257a384f4a8d4a0cbb44e2a1b3c154e5a967777f6bfe5bcdb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fd613b89e2113a257a384f4a8d4a0cbb44e2a1b3c154e5a967777f6bfe5bcdb","first_computed_at":"2026-07-05T10:59:32.051042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:59:32.051042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"07M1Whdg1CNIzC3pXNJcG7na+M6Ye0C0Ht9W7d51v9nXXEhGhBP7iLIBPH8QREG3m3itdtalsyM8lbEhHissCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:59:32.051546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.03816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1898b554045f43535c62f2c292d1f9424d457a03fbb4e3f24eb2668f866979b8","sha256:05464a9ff757bcf756a1f70272ce6242653245320143615d77218f62668dbff9"],"state_sha256":"6ebe045543452dc1c44b93779a9717bd2225c2a567f12a2e9e2c6e1e3025b352"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"npuKyCgNlDrdftp9zYDX3a+S8E8svM0GZfdd2HO3+T3Vbd5QP8ZWosjd1dzsezmHCDh3ShaS8NriVR/NXjLrBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:31:24.179014Z","bundle_sha256":"ac909a06c4e1abd5461ce418811d90f2f9c2eef7867359db38ef213139c9472c"}}