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Transportation mode detection – an in-depth review of applicability and reliability
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0002-0916-0188
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)ORCID iD: 0000-0003-1164-8403
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0001-7124-7164
(English)In: Transport reviews, ISSN 0144-1647, E-ISSN 1464-5327Article in journal (Refereed) In press
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.

Keyword [en]
Transportation mode detection, transportation segmentation, location-based services, transportation science, human geography
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-196665DOI: 10.1080/01441647.2016.1246489OAI: oai:DiVA.org:kth-196665DiVA: diva2:1047293
Note

QC 20161121

Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2016-11-21Bibliographically approved

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Prelipcean, Adrian CorneliuGidofalvi, GyözöSusilo, Yusak Octavius
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