Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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.

Place, publisher, year, edition, pages
Taylor & Francis Group.
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: 10.1080/01441647.2016.1246489ISI: 000396893800003OAI: oai:DiVA.org:kth-196665DiVA, id: diva2:1047293
Note

QC 20161121

Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2018-05-07Bibliographically approved
In thesis
1. MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis
Open this publication in new window or tab >>MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
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).

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 48
Series
TRITA-ABE-DLT ; 2018:13
Keyword
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
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science; Computer Science; Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-227294 (URN)978-91-7729-793-2 (ISBN)
Public defence
2018-06-05, L1, Drottning Kristinas väg 30, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20180507

Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-05-07Bibliographically approved

Open Access in DiVA

transport_reviews(452 kB)145 downloads
File information
File name FULLTEXT01.pdfFile size 452 kBChecksum SHA-512
f9f7d4130db88e7687e9101bedf7ddcd5b1c7f132668c66ea84a1574610c1eb2e1b78b996e8d43f49f60e706baab5ef949e9d0970328916c2b28e15e1fee9a90
Type fulltextMimetype application/pdf

Other links

Publisher's full textTransportation Reviews

Search in DiVA

By author/editor
Prelipcean, Adrian CorneliuGidofalvi, GyözöSusilo, Yusak Octavius
By organisation
Geodesy and GeoinformaticsSystem Analysis and Economics
In the same journal
Transport reviews
Transport Systems and Logistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 145 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 76 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf