Transport behavior-mining from smartphones: a review

Abstract Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the rich...

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Autores principales:	Valentino Servizi, Francisco C. Pereira, Marie K. Anderson, Otto A. Nielsen
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Smartphone-based travel surveys Machine learning User behavior Transport Map-matching Mode detection Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/16bbcfee4dab4f1285bfc92e5f514a01
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id	oai:doaj.org-article:16bbcfee4dab4f1285bfc92e5f514a01
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spelling	oai:doaj.org-article:16bbcfee4dab4f1285bfc92e5f514a012021-11-08T10:45:30ZTransport behavior-mining from smartphones: a review10.1186/s12544-021-00516-z1867-07171866-8887https://doaj.org/article/16bbcfee4dab4f1285bfc92e5f514a012021-11-01T00:00:00Zhttps://doi.org/10.1186/s12544-021-00516-zhttps://doaj.org/toc/1867-0717https://doaj.org/toc/1866-8887Abstract Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the richest perspective on the study of inter- and intrauser behavioral variations. Yet after over a decade of research and field experimentation on such surveys, and despite a consensus in transportation research as to their potential, smartphone-based travel surveys are seldom used on a large scale. Purpose This literature review pinpoints and examines the problems limiting prior research, and exposes drivers to select and rank machine-learning algorithms used for data processing in smartphone-based surveys. Conclusion Our findings show the main physical limitations from a device perspective; the methodological framework deployed for the automatic generation of travel-diaries, from the application perspective; and the relationship among user interaction, methods, and data, from the ground truth perspective.Valentino ServiziFrancisco C. PereiraMarie K. AndersonOtto A. NielsenSpringerOpenarticleSmartphone-based travel surveysMachine learningUser behaviorTransportMap-matchingMode detectionTransportation engineeringTA1001-1280Transportation and communicationsHE1-9990ENEuropean Transport Research Review, Vol 13, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Smartphone-based travel surveys Machine learning User behavior Transport Map-matching Mode detection Transportation engineering TA1001-1280 Transportation and communications HE1-9990
spellingShingle	Smartphone-based travel surveys Machine learning User behavior Transport Map-matching Mode detection Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Valentino Servizi Francisco C. Pereira Marie K. Anderson Otto A. Nielsen Transport behavior-mining from smartphones: a review
description	Abstract Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the richest perspective on the study of inter- and intrauser behavioral variations. Yet after over a decade of research and field experimentation on such surveys, and despite a consensus in transportation research as to their potential, smartphone-based travel surveys are seldom used on a large scale. Purpose This literature review pinpoints and examines the problems limiting prior research, and exposes drivers to select and rank machine-learning algorithms used for data processing in smartphone-based surveys. Conclusion Our findings show the main physical limitations from a device perspective; the methodological framework deployed for the automatic generation of travel-diaries, from the application perspective; and the relationship among user interaction, methods, and data, from the ground truth perspective.
format	article
author	Valentino Servizi Francisco C. Pereira Marie K. Anderson Otto A. Nielsen
author_facet	Valentino Servizi Francisco C. Pereira Marie K. Anderson Otto A. Nielsen
author_sort	Valentino Servizi
title	Transport behavior-mining from smartphones: a review
title_short	Transport behavior-mining from smartphones: a review
title_full	Transport behavior-mining from smartphones: a review
title_fullStr	Transport behavior-mining from smartphones: a review
title_full_unstemmed	Transport behavior-mining from smartphones: a review
title_sort	transport behavior-mining from smartphones: a review
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/16bbcfee4dab4f1285bfc92e5f514a01
work_keys_str_mv	AT valentinoservizi transportbehaviorminingfromsmartphonesareview AT franciscocpereira transportbehaviorminingfromsmartphonesareview AT mariekanderson transportbehaviorminingfromsmartphonesareview AT ottoanielsen transportbehaviorminingfromsmartphonesareview
_version_	1718442590948294656

Transport behavior-mining from smartphones: a review

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