{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T21:09:08Z","timestamp":1761340148973,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future.<\/jats:p>","DOI":"10.3390\/s21030844","type":"journal-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T12:20:26Z","timestamp":1611750026000},"page":"844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating Bus Cross-Sectional Flow Based on Machine Learning Algorithm Combined with Wi-Fi Probe Technology"],"prefix":"10.3390","volume":"21","author":[{"given":"Ting-Zhao","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Yan-Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3189-8872","authenticated-orcid":false,"given":"Jian-Hui","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.trc.2010.12.003","article-title":"Smart card data use in public transit: A literature review","volume":"19","author":"Pelletier","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_2","unstructured":"Shi, F. (2004). The Research of the Method of Generating the Public Transport Travel OD Matrix Based on the Data of IC Card, Jilin University."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Salamah, A.H., Tamazin, M., Sharkas, M.A., and Khedr, M. (2016, January 4\u20137). An enhanced wifi indoor localization system based on machine learning. Proceedings of the International Conference on Indoor Positioning & Indoor Navigation IEEE, Madrid, Spain.","DOI":"10.1109\/IPIN.2016.7743586"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4746","DOI":"10.1109\/JSEN.2018.2813983","article-title":"Wi-fi sensing-based real-time bus tracking and arrival time prediction in urban environments","volume":"18","author":"Zhang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_5","first-page":"282","article-title":"Predicting bus arrival time with mobile phone based participatory sensing","volume":"4","author":"Ramaiah","year":"2016","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_6","unstructured":"Reiff, R.M. (2012, January 12\u201315). Determination of origin-destination using bluetooth technology. Proceedings of the ITE 2012 Annual Meeting & Exhibit Institute of Transportation Engineers (ITE), Atlanta, GA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Campana, F., Pinargote, A., Dominguez, F., and Pelaez, E. (2017, January 16\u201320). Towards an indoor navigation system using bluetooth low energy beacons. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.","DOI":"10.1109\/ETCM.2017.8247464"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.apgeog.2014.04.001","article-title":"Tracking spatio-temporal movement of human in terms of space utilization using Media-Access-Control address data","volume":"51","author":"Abedi","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.trc.2015.08.010","article-title":"Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses","volume":"60","author":"Abedi","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TPAMI.2007.1174","article-title":"Multicamera people tracking with a probabilistic occupancy map","volume":"30","author":"Fleuret","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Takala, V., and Pietikainen, M. (2007, January 17\u201322). Multi-object tracking using color. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383506"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"163675","DOI":"10.1016\/j.ijleo.2019.163675","article-title":"Passenger flow counting in buses based on deep learning using surveillance video","volume":"202","author":"Hsu","year":"2019","journal-title":"Opt. Int. J. Light Electr. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhu, J., Feng, F., and Shen, B. (2018, January 18\u201320). People counting and pedestrian flow statistics based on convolutional neural network and recurrent neural network. Proceedings of the Youth Academic Annual Conference of Chinese Association of Automation, (YAC), Nanjing, China.","DOI":"10.1109\/YAC.2018.8406516"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2017.07.007","article-title":"A survey of recent advances in CNN-based single image crowd counting and density estimation","volume":"107","author":"Sindagi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.trb.2006.04.001","article-title":"A generalized and efficient algorithm for estimating transit Route ODs from passenger counts","volume":"41","author":"Li","year":"2007","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/23249935.2018.1537319","article-title":"Crowding valuation in urban tram and bus transportation based on smart card data","volume":"16","author":"Yap","year":"2020","journal-title":"Transportmetr. A Transp. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Myrvoll, T.A., Hakegard, J.E., Matsui, T., and Septier, F. (2017, January 16\u201319). Counting public transport passenger using WiFi signatures of mobile devices. Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317687"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1109\/TITS.2018.2879497","article-title":"Forecasting short-term passenger flow: An empirical study on Shenzhen Metro","volume":"20","author":"Tang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Song, C.J., and Wang, J. (2017). WLAN fingerprint indoor positioning strategy based on implicit crowdsourcing and semi-supervised learning. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110356"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jedari, E., Zheng, W., Rashid, R., and Mehrdad, S. (2015, January 13\u201316). Wi-Fi based indoor location positioning employing random forest classifier. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Banff, AB, Canada.","DOI":"10.1109\/IPIN.2015.7346754"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Youssef, M., and Agrawala, A. (2008). The Horus Location Determination System, Springer Wireless Networks.","DOI":"10.1007\/s11276-006-0725-7"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., and Smola, A. (2002). Learning with Kernels, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/844\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:16:06Z","timestamp":1760159766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,27]]},"references-count":22,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030844"],"URL":"https:\/\/doi.org\/10.3390\/s21030844","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,27]]}}}