Commute trips in Norwegian cities: Data combining trip characteristics and revealed mode choice by Erik Bjørnson Lunke, Nils Fearnley, Jørgen Aarhaug

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Morphologie urbaine et mobilité dans la Communauté Urbaine de Strasbourg by Dong-Binh Tran, Arnaud Piombini, Michal Ignatowicz, Diego Moreno, Rassil Frigui, Anne Aguiléra, Dominique Badariotti

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App-based automatic collection of travel behaviour: A field study comparison with self-reported behaviour by Ingeborg Storesund Hesjevoll, Aslak Fyhri, Alice Ciccone

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Development of a neighborhood drivability index and its association with transportation behavior in Toronto by Nicolette R. den Braver, Jeroen Lakerveld, Peter Gozdyra, Tim van de Brug, John S. Moin, Ghazal S. Fazli, Femke Rutters, Johannes Brug, Rahim Moineddin, Joline W.J. Beulens, Gillian L. Booth

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A review of travel behavioural pattern using GPS dataset: A systematic literature review by Prahaladhan Sivalingam, David Asirvatham, Mohsen Marjani, Jafar Ali Ibrahim Syed Masood, N.S. Kalyan Chakravarthy, Gopinath Veerisetty, Martha Tri Lestari

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Characterization of travel behavior at a university campus: A case study of Sharjah University City, UAE by Khaled Hamad, Phyo Thet Thet Htun, Lubna Obaid

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Changes in the travel patterns of older Americans with medical conditions: A comparison of 2001 and 2017 NHTS data by Abhay Lidbe, Emmanuel Kofi Adanu, Praveena Penmetsa, Steven Jones

Subjects: “…National Household Travel Survey (NHTS)…”
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Le plafonnement de la mobilité des citadins : changements de comportement ou effets de structure ? by Irving Tapia-Villareal, Jean-Loup Madre, Yves D. Bussière

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Are self-reported times rounded? Insights from times reported by an objective third party by Nobuhiro Sanko, Naotaka Iriguchi

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The stay coefficient: a novel quantification of the relationship between stay time and travel time for urban shopping behavior analysis by Takashi Yamada

Subjects: “…Travel survey…”
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Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm by Fang Zong, Yu Bai, Xiao Wang, Yixin Yuan, Yanan He

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Machine learning approaches for predicting household transportation energy use by Shideh Shams Amiri, Nariman Mostafavi, Earl Rusty Lee, Simi Hoque

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Impact of travel mode shift and trip distance on active and non-active transportation in the São Paulo Metropolitan Area in Brazil by Thiago Hérick de Sá, Diana C. Parra, Carlos Augusto Monteiro

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Large-Scale Mobile-Based Analysis for National Travel Demand Modeling by Bat-hen Nahmias-Biran, Shuki Cohen, Vladimir Simon, Israel Feldman

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Transport behavior-mining from smartphones: a review by Valentino Servizi, Francisco C. Pereira, Marie K. Anderson, Otto A. Nielsen

Subjects: “…Smartphone-based travel surveys…”
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Évaluation comparée de l’apport de l’assistance GPS aux enquêtes de mobilité by Guillaume Drevon, Francis Jambon, Sonia Chardonnel, Sidonie Christophe, Isabelle André-Poyaud, Paule-Annick Davoine, Céline Lutoff

“…Travel surveys can collect information about practices and behaviours of individual mobility. …”
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Validity of instruments to assess students' travel and pedestrian safety by Baranowski Tom, Watson Kathy, Mendoza Jason A, Nicklas Theresa A, Uscanga Doris K, Hanfling Marcus J

“…This research validated the SRTS school travel survey and a pedestrian safety behavior checklist.…”
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A weighted travel time index based on data from Uber Movement by Renato S. Vieira, Eduardo A. Haddad

“…Abstract In this paper, we combine data from Uber Movement and from a representative household travel survey to constructs a weighted travel time index for the Metropolitan Region of São Paulo. …”
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Estimating car ownership and transport energy consumption: a disaggregate study in Nelson Mandela Bay by C J Venter, S O Mohammed

“…A methodology is developed to estimate trip-by-trip energy consumption using standard 24-hour travel survey data, and applied to the Nelson Mandela Metropolitan Area using their 2004 household travel survey. …”
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Determining Commuting Behaviour from Monitoring Technologies by Yuting You, Wei Wang, Min Yang, Russell G. Thompson

“…This paper, with the sample data collected by The Australian Diabetes, Obesity and Lifestyle (AusDiab) study, discusses the process of how to utilize data obtained from GPS and inclinometer device, along with basic information about participants to conduct travel survey, and reconstructing participant's commuting behaviour. …”
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