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Transportation mode detection – an in-depth review of applicability and reliability
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geodesi och geoinformatik. KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap, Transport- och lokaliseringsanalys.ORCID-id: 0000-0002-0916-0188
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.ORCID-id: 0000-0003-1164-8403
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap, Transport- och lokaliseringsanalys.ORCID-id: 0000-0001-7124-7164
2017 (Engelska)Ingår i: Transport reviews, ISSN 0144-1647, E-ISSN 1464-5327, Vol. 37, nr 4, s. 442-464Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency, ethics and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g. transportation mode detection, service contextualisation, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location-Based Services (LBS) is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g. the real-time availability of LBS requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects desired by each research field when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions, that is, proposing: widely accepted error measures meaningful for all disciplines, methods robust to new data sets and a benchmark data set for performance validation.

Ort, förlag, år, upplaga, sidor
Taylor & Francis Group, 2017. Vol. 37, nr 4, s. 442-464
Nyckelord [en]
Transportation mode detection, transportation segmentation, location-based services, transportation science, human geography
Nationell ämneskategori
Transportteknik och logistik
Forskningsämne
Transportvetenskap
Identifikatorer
URN: urn:nbn:se:kth:diva-196665DOI: 10.1080/01441647.2016.1246489ISI: 000396893800003Scopus ID: 2-s2.0-84992372044OAI: oai:DiVA.org:kth-196665DiVA, id: diva2:1047293
Anmärkning

QC 20161121

Tillgänglig från: 2016-11-17 Skapad: 2016-11-17 Senast uppdaterad: 2024-03-18Bibliografiskt granskad
Ingår i avhandling
1. MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis
Öppna denna publikation i ny flik eller fönster >>MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Researchers' pursuit for the better understanding of the dynamics of travel and travel behaviour led to a constant advance in data collection methods. One such data collection method, the travel diary, is a common proxy for travel behaviour and its use has a long history in the transportation research community. These diaries summarize information about when, where, why and how people travel by collecting information about trips, and their destination and purpose, and triplegs, and their travel mode. Whereas collecting travel diaries for short periods of time of one day was commonplace due to the high cost of conducting travel surveys, visionary researchers have tried to better understand whether travel and travel behaviour is stable or if, and how, it changes over time by collecting multiple day travel diaries from the same users. While the initial results of these researchers were promising, the high cost of travel surveys and the fill in burden of the survey participants limited the research contribution to the scientific community. Before identifying travel diary collection methods that can be used for long periods of time, an interesting phenomenon started to occur: a steady decrease in the response rate to travel diaries. This meant that the pursuit of understanding the evolution of travel behaviour over time stayed in the scientific community and did not evolve to be used by policy makers and industrial partners.

However, with the development of technologies that can collect trajectory data that describe how people travel, researchers have investigated ways to complement and replace the traditional travel diary collection methods. While the initial efforts were only partially successful because scientists had to convince people to carry devices that they were not used to, the wide adoption of smartphones opened up the possibility of wide-scale trajectory-based travel diary collection and, potentially, for long periods of time. This thesis contributes among the same direction by proposing MEILI, a travel diary collection system, and describes the trajectory collection outlet (Paper I) and the system architecture (Paper II). Furthermore, the process of transforming a trajectory into travel diaries by using machine learning is thoroughly documented (Papers III and IV), together with a robust and objective methodology for comparing different travel diary collection system (Papers V and VI). MEILI is presented in the context of current state of the art (Paper VIII) and the researchers' common interest (Paper IX), and has been used in various case studies for collecting travel diaries (Papers I, V, VI, VII). Finally, since MEILI has been successfully used for collecting travel diaries for a period of one week, a new method for understanding the stability and variability of travel patterns over time has been proposed (Paper X).

Ort, förlag, år, upplaga, sidor
KTH Royal Institute of Technology, 2018. s. 48
Serie
TRITA-ABE-DLT ; 2018:13
Nyckelord
multiple day travel diary collection, trajectory segmentation, travel mode destination and purpose inference, travel diary collection system comparison, travel pattern stability and variability over time
Nationell ämneskategori
Transportteknik och logistik Datavetenskap (datalogi)
Forskningsämne
Transportvetenskap; Datalogi; Geodesi och geoinformatik
Identifikatorer
urn:nbn:se:kth:diva-227294 (URN)978-91-7729-793-2 (ISBN)
Disputation
2018-06-05, L1, Drottning Kristinas väg 30, Stockholm, 13:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20180507

Tillgänglig från: 2018-05-07 Skapad: 2018-05-07 Senast uppdaterad: 2022-06-26Bibliografiskt granskad

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