{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IBG5KXGTOX2JX4V7C7CIMYYQJY","short_pith_number":"pith:IBG5KXGT","schema_version":"1.0","canonical_sha256":"404dd55cd375f49bf2bf17c48663104e14225d81c1d21c37dfbf138911dac103","source":{"kind":"arxiv","id":"2111.03204","version":1},"attestation_state":"computed","paper":{"title":"Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Enpeng Yuan, Pascal Van Hentenryck","submitted_at":"2021-11-05T00:52:15Z","abstract_excerpt":"Large-scale ride-hailing systems often combine real-time routing at the individual request level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing and vehicle relocation. The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization. However, the longer horizon increases computational complexity and forces the MPC to operate at coarser spatial-temporal granularity, degrading the quality of its decisions. This paper addresses these computational challenges by learning the MPC optimiza"},"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":"2111.03204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-05T00:52:15Z","cross_cats_sorted":[],"title_canon_sha256":"51056f4dc76622b495dc781fa4ee50782a1b78befbbc997400941eed4c718f47","abstract_canon_sha256":"f6b6644f299feecfa14ad3998cea2251f5e11e59daf54834f7874161bb46efa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:29:21.828767Z","signature_b64":"OCrcTS8QlnVeODo9V7AjES9q+MrUqI+o/9xh/kTpWwzdgM2Gm83/qUhlPgV6AGk+R96NwxdFeXEgaoPIpmmUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"404dd55cd375f49bf2bf17c48663104e14225d81c1d21c37dfbf138911dac103","last_reissued_at":"2026-07-05T03:29:21.828389Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:29:21.828389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Enpeng Yuan, Pascal Van Hentenryck","submitted_at":"2021-11-05T00:52:15Z","abstract_excerpt":"Large-scale ride-hailing systems often combine real-time routing at the individual request level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing and vehicle relocation. The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization. However, the longer horizon increases computational complexity and forces the MPC to operate at coarser spatial-temporal granularity, degrading the quality of its decisions. This paper addresses these computational challenges by learning the MPC optimiza"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.03204","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/2111.03204/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":"2111.03204","created_at":"2026-07-05T03:29:21.828456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.03204v1","created_at":"2026-07-05T03:29:21.828456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.03204","created_at":"2026-07-05T03:29:21.828456+00:00"},{"alias_kind":"pith_short_12","alias_value":"IBG5KXGTOX2J","created_at":"2026-07-05T03:29:21.828456+00:00"},{"alias_kind":"pith_short_16","alias_value":"IBG5KXGTOX2JX4V7","created_at":"2026-07-05T03:29:21.828456+00:00"},{"alias_kind":"pith_short_8","alias_value":"IBG5KXGT","created_at":"2026-07-05T03:29:21.828456+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/IBG5KXGTOX2JX4V7C7CIMYYQJY","json":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY.json","graph_json":"https://pith.science/api/pith-number/IBG5KXGTOX2JX4V7C7CIMYYQJY/graph.json","events_json":"https://pith.science/api/pith-number/IBG5KXGTOX2JX4V7C7CIMYYQJY/events.json","paper":"https://pith.science/paper/IBG5KXGT"},"agent_actions":{"view_html":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY","download_json":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY.json","view_paper":"https://pith.science/paper/IBG5KXGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.03204&json=true","fetch_graph":"https://pith.science/api/pith-number/IBG5KXGTOX2JX4V7C7CIMYYQJY/graph.json","fetch_events":"https://pith.science/api/pith-number/IBG5KXGTOX2JX4V7C7CIMYYQJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY/action/storage_attestation","attest_author":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY/action/author_attestation","sign_citation":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY/action/citation_signature","submit_replication":"https://pith.science/pith/IBG5KXGTOX2JX4V7C7CIMYYQJY/action/replication_record"}},"created_at":"2026-07-05T03:29:21.828456+00:00","updated_at":"2026-07-05T03:29:21.828456+00:00"}