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Mobility Collector
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)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
2014 (English)In: Journal of Location Based Services, ISSN 1748-9725, Vol. 8, no 4, 229-255 p.Article in journal (Refereed) Published
Abstract [en]

Despite the availability of mobile positioning technologies and scientists' interests in tracking, modelling and predicting the movements of individuals and populations, these technologies are seldom efficiently used. The continuous changes in mobile positioning and other sensor technologies overburden scientists who are interested in data collection with the task of developing, implementing and testing tracking algorithms and their efficiency in terms of battery consumption. To this extent, this article proposes an adaptive, battery conscious tracking algorithm that collects trajectory data fused with accelerometer data and presents Mobility Collector, which is a prototype platform that, using the tracking algorithm, can produce highly configurable, off-the-shelf, multi-user tracking systems suitable for research purposes. The applicability of the tracking system is tested within the transport science domain by collecting labelled movement traces and related motion data, i.e. accelerometer data and derived information (number of steps and other useful movement features based on temporal aggregates of the raw readings) to develop and evaluate a method that automatically classifies the transportation mode of users with a 90.8% prediction accuracy.

Place, publisher, year, edition, pages
2014. Vol. 8, no 4, 229-255 p.
Keyword [en]
battery conscious, data collection, location and accelerometer data fusion, location awareness, location tracking, movement, smartphones
National Category
Other Engineering and Technologies Computer Science
Research subject
Geodesy and Geoinformatics; Transport Science
URN: urn:nbn:se:kth:diva-161529DOI: 10.1080/17489725.2014.973917ScopusID: 2-s2.0-84914664420OAI: diva2:794679
Mobile Ghent 2013, Ghent, Belgium, 23-25 October, 2013
TrenOp, Transport Research Environment with Novel Perspectives

QC 20150312

Available from: 2015-03-12 Created: 2015-03-12 Last updated: 2016-05-25Bibliographically approved
In thesis
1. Capturing travel entities to facilitate travel behaviour analysis: A case study on generating travel diaries from trajectories fused with accelerometer readings
Open this publication in new window or tab >>Capturing travel entities to facilitate travel behaviour analysis: A case study on generating travel diaries from trajectories fused with accelerometer readings
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The increase in population, accompanied by an increase in the availability of travel opportunities have kindled the interest in understanding how people make use of the space around them and their opportunities. Understanding the travel behaviour of individuals and groups is difficult because of two main factors: the travel behaviour's wide coverage, which encompasses different research areas, all of which model different aspects of travel behaviour, and the difficulty of obtaining travel diaries from large groups of respondents, which is imperative for analysing travel behaviour and patterns.

A travel diary allows an individual to describe how she performed her activities by specifying the destinations, purposes and travel modes occurring during a predefined period of time. Travel diaries are usually collected during a large-scale survey, but recent developments show that travel diaries have important drawbacks such as the collection bias and the decreasing response rate. This led to a surge of studies that try to complement or replace the traditional declaration-based travel diary collection with methods that extract travel diary specific information from trajectories and auxiliary datasets.

With the automation of travel diary generation in sight, this thesis presents a suitable method for collecting data for travel diary automation (Paper I), a framework to compare multiple travel diary collection systems (Paper II), a set of relevant metrics for measuring the performance of travel mode segmentation methods (Paper III), and applies these concepts during different case studies (Paper IV).

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 88 p.
TRITA-SOM, ISSN 1653-6126 ; 2016-05
travel diary automation, trajectory segmentation, travel data collection, travel diary collection system evaluation and comparison
National Category
Transport Systems and Logistics Computer Science Human Geography
Research subject
Geodesy and Geoinformatics
urn:nbn:se:kth:diva-187491 (URN)978-91-7595-958-0 (ISBN)
2016-06-07, L1, Drottning Kristinas väg 30, Stockholm, 09:00 (English)

QC 20160525

Available from: 2016-05-25 Created: 2016-05-24 Last updated: 2016-05-25Bibliographically approved

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