{"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 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