{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:09:17Z","timestamp":1772795357840,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,8]],"date-time":"2017-09-08T00:00:00Z","timestamp":1504828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000930","name":"NSF","doi-asserted-by":"publisher","award":["DMS-1156701"],"award-info":[{"award-number":["DMS-1156701"]}],"id":[{"id":"10.13039\/501100000930","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Minnesota, Institute for Mathematics and Its Applications"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy.<\/jats:p>","DOI":"10.3390\/s17092058","type":"journal-article","created":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T02:01:30Z","timestamp":1505095290000},"page":"2058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8002-5296","authenticated-orcid":false,"given":"Bryan","family":"Martin","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-9797","authenticated-orcid":false,"given":"Vittorio","family":"Addona","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, MN 55105-1899, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julian","family":"Wolfson","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0341, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gediminas","family":"Adomavicius","sequence":"additional","affiliation":[{"name":"Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN 55455-0438, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingling","family":"Fan","sequence":"additional","affiliation":[{"name":"Humphrey School of Public Affairs, University of Minnesota, MN 55455-0395, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,8]]},"reference":[{"key":"ref_1","unstructured":"Applied Management & Planning Group (1995). AMPG Report to NCTCOG \u201cDallas-Fort Worth Household Travel Survey Pretest Report\u201d, Applied Management & Planning Group."},{"key":"ref_2","unstructured":"Kitamura, R. (1995). Time-of-Day Characteristics of Travel: An Analysis of 1990 NPTS Data, Chapter 4, Federal Highway Administration."},{"key":"ref_3","unstructured":"Murakami, E., Wagner, D.P., and Neumeister, D.M. (2004, January 24\u201330). Using global positioning systems and personal digital assistants for personal travel surveys in the United States. Proceedings of the International Conference on Transport Survey Quality and Innovation, Grainau, Germany."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the Second International Conference, PERVASIVE 2004, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_5","unstructured":"Krishnan, N.C., Colbry, D., Juillard, C., and Panchanathan, S. (,  2008). Real time human activity recognition using tri-axial accelerometers. Proceedings of the Sensors, Signals and Information Processing Workshop, Sedona, AZ, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brezmes, T., Gorricho, J.L., and Cotrina, J. (2009). Activity recognition from accelerometer data on a mobile phone. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, Springer.","DOI":"10.1007\/978-3-642-02481-8_120"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11036-008-0112-y","article-title":"An activity recognition system for mobile phones","volume":"14","year":"2009","journal-title":"Mob. Netw. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Patterson, D.J., Liao, L., Fox, D., and Kautz, H. (2003). Inferring high-level behavior from low-level sensors. UbiComp 2003: Ubiquitous Computing, Springer.","DOI":"10.1007\/978-3-540-39653-6_6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sohn, T., Varshavsky, A., LaMarca, A., Chen, M.Y., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W.G., and De Lara, E. (2006). Mobility detection using everyday gsm traces. UbiComp 2006: Ubiquitous Computing, Springer.","DOI":"10.1007\/11853565_13"},{"key":"ref_11","unstructured":"Anderson, I., and Muller, H. (2006). Practical Activity Recognition Using GSM Data, University of Bristol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W.Y. (2008, January 21\u201324). Understanding mobility based on GPS data. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409677"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1689239.1689243","article-title":"Using mobile phones to determine transportation modes","volume":"6","author":"Reddy","year":"2010","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.trpro.2015.09.073","article-title":"Modelling of accelerometer data for travel mode detection by hierarchical application of binomial logistic regression","volume":"10","author":"Shafique","year":"2015","journal-title":"Transp. Res. Procedia"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11116-014-9541-6","article-title":"Use of acceleration data for transportation mode prediction","volume":"42","author":"Shafique","year":"2015","journal-title":"Transportation"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.trc.2013.09.014","article-title":"Transportation mode recognition using GPS and accelerometer data","volume":"37","author":"Feng","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20843","DOI":"10.3390\/s141120843","article-title":"Using smart phone sensors to detect transportation modes","volume":"14","author":"Xia","year":"2014","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2012, January 3\u20135). Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Proceedings of the International Workshop on Ambient Assisted Living, Vitoria-Gasteiz, Spain.","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"ref_19","unstructured":"(2017, June 01). ActivityRecognitionApi. Available online: https:\/\/developers.google.com\/android\/reference\/com\/google\/android\/gms\/location\/ActivityRecognitionApi."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/fpubh.2014.00036","article-title":"Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms","volume":"2","author":"Ellis","year":"2014","journal-title":"Front. Public Health"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1214\/12-EJS684","article-title":"Movelets: A dictionary of movement","volume":"6","author":"Bai","year":"2012","journal-title":"Electron. J. Stat."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional Variable Importance for Random Forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_25","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_26","unstructured":"(2017, June 01). Caret: Classification and Regression Training. Available online: https:\/\/CRAN.R-project.org\/package=caret."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:24Z","timestamp":1760208264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,8]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["s17092058"],"URL":"https:\/\/doi.org\/10.3390\/s17092058","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,8]]}}}