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In this paper, we propose a novel approach to indoor positioning that leverages fingerprints only sampled outdoors, which can be collected through crowdsourcing within a ride-hailing platform. This approach significantly reduces deployment costs, enables timely updates to the fingerprint set, and provides unprecedented accessibility. We address three key challenges in this system, including using outdoor fingerprints to estimate indoor position, abnormal Access Points (APs), and existence of \"blackholes\" where overheard APs have no fingerprint. Our implementation, built on the DiDi ride-hailing platform, is evaluated through extensive experiments with 122 million orders across 13 million devices in multiple cities. The results demonstrate that our system achieves a significant reduction of 4.35m in pickup position error compared to existing efforts, showcasing its potential for large-scale adoption.<\/jats:p>","DOI":"10.1145\/3729498","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:21:56Z","timestamp":1750281716000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Large-Scale Indoor Localization via Outdoor Crowdsourcing Trajectories on Ride-Hailing Platform"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-9224","authenticated-orcid":false,"given":"Shuli","family":"Zhu","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5754-8451","authenticated-orcid":false,"given":"Lingkun","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-8674","authenticated-orcid":false,"given":"Xuyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-1656","authenticated-orcid":false,"given":"Qiang","family":"Ni","sequence":"additional","affiliation":[{"name":"Lancaster University, Lancaster, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6800-5598","authenticated-orcid":false,"given":"Yuqin","family":"Jiang","sequence":"additional","affiliation":[{"name":"DiDi Company, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1278-0787","authenticated-orcid":false,"given":"Hui","family":"Gao","sequence":"additional","affiliation":[{"name":"DiDi Company, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4207-1482","authenticated-orcid":false,"given":"Zhaobing","family":"Han","sequence":"additional","affiliation":[{"name":"DiDi Company, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2490-6654","authenticated-orcid":false,"given":"Ruipeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN51156.2021.9662616"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"e_1_2_2_4_1","first-page":"1","article-title":"Deep learning based wireless localization for indoor navigation","volume":"17","author":"Ayyalasomayajula Roshan","year":"2020","unstructured":"Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, and Dinesh Bharadia. 2020. 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