{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:11:17Z","timestamp":1790745077601,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"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>In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user\u2019s own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification.<\/jats:p>","DOI":"10.3390\/s20247228","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T10:42:47Z","timestamp":1608201767000},"page":"7228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"given":"Francesco","family":"Delli Priscoli","sequence":"first","affiliation":[{"name":"Control and Management Engineering \u201cAntonio Ruberti\u201d, Department of Computer, University of Rome \u201cLa Sapienza\u201d, Via Ariosto 25, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5503-8506","authenticated-orcid":false,"given":"Alessandro","family":"Giuseppi","sequence":"additional","affiliation":[{"name":"Control and Management Engineering \u201cAntonio Ruberti\u201d, Department of Computer, University of Rome \u201cLa Sapienza\u201d, Via Ariosto 25, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Lisi","sequence":"additional","affiliation":[{"name":"Control and Management Engineering \u201cAntonio Ruberti\u201d, Department of Computer, University of Rome \u201cLa Sapienza\u201d, Via Ariosto 25, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Mobile Edge Computing A key technology towards 5G","volume":"11","author":"Hu","year":"2015","journal-title":"ETSI White Pap."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Black, J. 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