{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:JUSXYY3C5EB2Q5F2YT43O2BH2Y","short_pith_number":"pith:JUSXYY3C","schema_version":"1.0","canonical_sha256":"4d257c6362e903a874bac4f9b76827d63d003c5f268fb29cea3f449c24141511","source":{"kind":"arxiv","id":"2010.09847","version":1},"attestation_state":"computed","paper":{"title":"Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ikjin Lee, Namwoo Kang, Seongsin Kim, Ungki Lee","submitted_at":"2020-10-16T05:06:58Z","abstract_excerpt":"In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction mode"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2010.09847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T05:06:58Z","cross_cats_sorted":[],"title_canon_sha256":"52beccb7260605cc232e524b401c9db51d9f3b148b39acb49e248bbb529478ee","abstract_canon_sha256":"6c82dd285decf7d1a3330f844fcccdec48caea9f62481fe1123e1cb6c9d151cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:38.404919Z","signature_b64":"4+bRPua09xbg/7U7y/ggORXHhOboIkivpeWUtq1jGH1uuyZn2FSwZCEil93+1/Az3oedzjZ++odoSWLuvuLBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d257c6362e903a874bac4f9b76827d63d003c5f268fb29cea3f449c24141511","last_reissued_at":"2026-07-05T05:02:38.404498Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:38.404498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ikjin Lee, Namwoo Kang, Seongsin Kim, Ungki Lee","submitted_at":"2020-10-16T05:06:58Z","abstract_excerpt":"In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09847","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/2010.09847/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.09847","created_at":"2026-07-05T05:02:38.404552+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.09847v1","created_at":"2026-07-05T05:02:38.404552+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09847","created_at":"2026-07-05T05:02:38.404552+00:00"},{"alias_kind":"pith_short_12","alias_value":"JUSXYY3C5EB2","created_at":"2026-07-05T05:02:38.404552+00:00"},{"alias_kind":"pith_short_16","alias_value":"JUSXYY3C5EB2Q5F2","created_at":"2026-07-05T05:02:38.404552+00:00"},{"alias_kind":"pith_short_8","alias_value":"JUSXYY3C","created_at":"2026-07-05T05:02:38.404552+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y","json":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y.json","graph_json":"https://pith.science/api/pith-number/JUSXYY3C5EB2Q5F2YT43O2BH2Y/graph.json","events_json":"https://pith.science/api/pith-number/JUSXYY3C5EB2Q5F2YT43O2BH2Y/events.json","paper":"https://pith.science/paper/JUSXYY3C"},"agent_actions":{"view_html":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y","download_json":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y.json","view_paper":"https://pith.science/paper/JUSXYY3C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.09847&json=true","fetch_graph":"https://pith.science/api/pith-number/JUSXYY3C5EB2Q5F2YT43O2BH2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/JUSXYY3C5EB2Q5F2YT43O2BH2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y/action/storage_attestation","attest_author":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y/action/author_attestation","sign_citation":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y/action/citation_signature","submit_replication":"https://pith.science/pith/JUSXYY3C5EB2Q5F2YT43O2BH2Y/action/replication_record"}},"created_at":"2026-07-05T05:02:38.404552+00:00","updated_at":"2026-07-05T05:02:38.404552+00:00"}