{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MPRZQYZXJNKLLLSCAKGHMMSUY3","short_pith_number":"pith:MPRZQYZX","schema_version":"1.0","canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","source":{"kind":"arxiv","id":"2605.15745","version":1},"attestation_state":"computed","paper":{"title":"The Robotaxi Placement Problem: Minimizing Expected ETA for Stochastic Demand","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Aaron Schild, Ali Kemal Sinop, Ioannis Caragiannis, Kostas Kollias, Mohammad Roghani","submitted_at":"2026-05-15T08:54:32Z","abstract_excerpt":"Autonomous ride-hailing platforms must strategically position idle robotaxis to minimize the wait times of prospective riders. We formalize this as the \\emph{robotaxi placement problem} ($k$-RP). Given a finite metric space and a demand distribution over its points, the goal is to position $k$ robotaxis to minimize the expected total distance in a perfect matching between the robotaxis and $k$ random riders. We present several theoretical results for this stochastic optimization problem. First, we observe that sampling robotaxi locations independently according to the demand distribution yield"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.15745","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"aadff434765507267b497901f79fea1a3d354232818fcb2bac45ea58dccea8ea","abstract_canon_sha256":"570070d40ed5ca67abea3b10917d803db061a8971f7c96cc33ff538de15f1b6d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:15.973730Z","signature_b64":"HbDxTjE9wzYDeuq6fFZMpxe1tNG+9aI7rd3GvnJlo7Eu/17m2i9AGPRoXnulnNVPjyeCbZb1unEXXull/Ej9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","last_reissued_at":"2026-05-20T00:01:15.972915Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:15.972915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Robotaxi Placement Problem: Minimizing Expected ETA for Stochastic Demand","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Aaron Schild, Ali Kemal Sinop, Ioannis Caragiannis, Kostas Kollias, Mohammad Roghani","submitted_at":"2026-05-15T08:54:32Z","abstract_excerpt":"Autonomous ride-hailing platforms must strategically position idle robotaxis to minimize the wait times of prospective riders. We formalize this as the \\emph{robotaxi placement problem} ($k$-RP). Given a finite metric space and a demand distribution over its points, the goal is to position $k$ robotaxis to minimize the expected total distance in a perfect matching between the robotaxis and $k$ random riders. We present several theoretical results for this stochastic optimization problem. First, we observe that sampling robotaxi locations independently according to the demand distribution yield"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2119b5e9acbddf25b4b6df4910a7654a79e460fd81cc08b98ae105574c254af3"},"source":{"id":"2605.15745","kind":"arxiv","version":1},"verdict":{"id":"02651422-421f-4ae2-8b4c-dcf52e083aa6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:30:33.624106Z","strongest_claim":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem.","one_line_summary":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation).","pith_extraction_headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15745/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.192404Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:40:58.375059Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.890640Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.975839Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ab0b039cd300c7472366ebd291ec8ae05a55373d678123324fcc2898d7b563ac"},"references":{"count":29,"sample":[{"doi":"","year":1984,"title":"Optimal matchings of random points.Combi- natorica, 4(4):259–264, 1984","work_id":"89ff4134-811c-45dc-9114-4013051ed5d8","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.Proceedings of the National Academy of Sciences, 114(3):462–467, 2017","work_id":"fdd137b1-6496-40d4-adc4-61ed705ba36e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1988,"title":"Bertsimas.Probabilistic combinatorial optimization problems","work_id":"1095217e-12ad-4792-a080-33f2c4d56525","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1990,"title":"Bertsimas, Patrick Jaillet, and Amedeo R","work_id":"f7c63399-415d-4d2f-82b9-24dc2d181f99","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Empty-car routing in ridesharing systems.Operations Research, 67(5):1437–1452, 2019","work_id":"5c0f40f6-5653-43d8-8a1a-d02508e0202b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":29,"snapshot_sha256":"1d1c509886e47006274ce556bb74fc4f28c6064dea11f403547ebfb58e8b7d90","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d6f994c907d988ae98ed9b237fd488418df78e45733ed225ba53eb7249fc1ac4"},"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":"2605.15745","created_at":"2026-05-20T00:01:15.973048+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.15745v1","created_at":"2026-05-20T00:01:15.973048+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15745","created_at":"2026-05-20T00:01:15.973048+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPRZQYZXJNKL","created_at":"2026-05-20T00:01:15.973048+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPRZQYZXJNKLLLSC","created_at":"2026-05-20T00:01:15.973048+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPRZQYZX","created_at":"2026-05-20T00:01:15.973048+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3","json":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3.json","graph_json":"https://pith.science/api/pith-number/MPRZQYZXJNKLLLSCAKGHMMSUY3/graph.json","events_json":"https://pith.science/api/pith-number/MPRZQYZXJNKLLLSCAKGHMMSUY3/events.json","paper":"https://pith.science/paper/MPRZQYZX"},"agent_actions":{"view_html":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3","download_json":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3.json","view_paper":"https://pith.science/paper/MPRZQYZX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.15745&json=true","fetch_graph":"https://pith.science/api/pith-number/MPRZQYZXJNKLLLSCAKGHMMSUY3/graph.json","fetch_events":"https://pith.science/api/pith-number/MPRZQYZXJNKLLLSCAKGHMMSUY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/action/storage_attestation","attest_author":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/action/author_attestation","sign_citation":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/action/citation_signature","submit_replication":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/action/replication_record"}},"created_at":"2026-05-20T00:01:15.973048+00:00","updated_at":"2026-05-20T00:01:15.973048+00:00"}