{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T15:59:32Z","timestamp":1773244772242,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["PMJPR2039"],"award-info":[{"award-number":["PMJPR2039"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper\u2019s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers\u2019 privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers\u2019 mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%).<\/jats:p>","DOI":"10.3390\/s22030881","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Estimating Congestion in a Fixed-Route Bus by Using BLE Signals"],"prefix":"10.3390","volume":"22","author":[{"given":"Yuji","family":"Kanamitsu","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan"}]},{"given":"Eigo","family":"Taya","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan"}]},{"given":"Koki","family":"Tachibana","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8834-5323","authenticated-orcid":false,"given":"Yugo","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan"},{"name":"JST PRESTO, Tokyo 102-0076, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3135-4915","authenticated-orcid":false,"given":"Yuki","family":"Matsuda","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan"},{"name":"JST PRESTO, Tokyo 102-0076, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8519-3352","authenticated-orcid":false,"given":"Hirohiko","family":"Suwa","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1579-3237","authenticated-orcid":false,"given":"Keiichi","family":"Yasumoto","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hsu, Y.W., Chen, Y.W., and Perng, J.W. 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