{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:07:25Z","timestamp":1760058445798,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52202401","22PJC081"],"award-info":[{"award-number":["52202401","22PJC081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Pujiang Program","award":["52202401","22PJC081"],"award-info":[{"award-number":["52202401","22PJC081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate estimation of passenger origin\u2013destination (OD) matrices is critical for optimizing public transportation systems, yet conventional methods face challenges, such as incomplete alighting data, high infrastructure costs, and privacy concerns. With existing GPS sensors and the additional deployment of a single low-cost Bluetooth sensor (10\u201320 US dollars) per bus, the proposed method can derive passenger OD flow without requiring passengers to tap in or tap out. The GPS sensor updates the bus locations, and the Bluetooth sensor receives signals from surrounding devices, including those onboard devices and nearby external devices. A Fuzzy C-Means clustering algorithm was employed to differentiate passenger and non-passenger devices based on detected indicators, such as detection frequency, signal strength, vehicular mobility, etc. Validation on Shanghai\u2019s Fengpu BRT line demonstrated 91.22\u201396.02% accuracy in boarding proportion estimation and 95.18\u201395.52% for alighting during peak hours. Compared to the historical data-based method, the proposed method achieved higher similarity to ground truth and reduced the mean squared error by 12.89\u201369.95%.<\/jats:p>","DOI":"10.3390\/s25082351","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T11:05:02Z","timestamp":1744110302000},"page":"2351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Bluetooth and GPS Sensors for Route-Level Passenger Origin\u2013Destination Flow Estimation"],"prefix":"10.3390","volume":"25","author":[{"given":"Junming","family":"Xu","sequence":"first","affiliation":[{"name":"Smart Urban Mobility Institute, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenxing","family":"Pan","sequence":"additional","affiliation":[{"name":"Shanghai Seari Intelligent System Co., Ltd., Shanghai 200063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6529-2468","authenticated-orcid":false,"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Smart Urban Mobility Institute, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Yang","sequence":"additional","affiliation":[{"name":"Smart Urban Mobility Institute, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103273","DOI":"10.1016\/j.trc.2021.103273","article-title":"Estimating the route-level passenger demand profile from bus dwell times","volume":"130","author":"Sun","year":"2025","journal-title":"Transp. 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