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Measures of transport mode segmentation of trajectories
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
2016 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 9, 1763-1784 p.Article in journal (Refereed) Published
Abstract [en]

Rooted in the philosophy of point- and segment-based approaches for transportation mode segmentation of trajectories, the measures that researchers have adopted to evaluate the quality of the results (1) are incomparable across approaches, hence slowing the progress in the field and (2) do not provide insight about the quality of the continuous transportation mode segmentation. To address these problems, this paper proposes new error measures that can be applied to measure how well a continuous transportation mode segmentation model performs. The error measures introduced are based on aligning multiple inferred continuous intervals to ground truth intervals, and measure the cardinality of the alignment and the spatial and temporal discrepancy between the corresponding aligned segments. The utility of this new way of computing errors is shown by evaluating the segmentation of three generic transportation mode segmentation approaches (implicit, explicit–holistic, and explicit–consensus-based transport mode segmentation), which can be implemented in a thick client architecture. Empirical evaluations on a large real-word data set reveal the superiority of explicit–consensus-based transport mode segmentation, which can be attributed to the explicit modeling of segments and transitions, which allows for a meaningful decomposition of the complex learning task.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2016. Vol. 30, no 9, 1763-1784 p.
Keyword [en]
Continuous model evaluation, transportation mode segmentation and detection, trajectory data mining, error analysis, interval algebra
National Category
Transport Systems and Logistics Computer Science Other Mathematics
Research subject
Geodesy and Geoinformatics; Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-184485DOI: 10.1080/13658816.2015.1137297ISI: 000378064300005Scopus ID: 2-s2.0-84958541974OAI: oai:DiVA.org:kth-184485DiVA: diva2:916117
Note

QC 20160509

Available from: 2016-03-31 Created: 2016-03-31 Last updated: 2017-11-30Bibliographically 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.
Series
TRITA-SOM, ISSN 1653-6126 ; 2016-05
Keyword
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
Identifiers
urn:nbn:se:kth:diva-187491 (URN)978-91-7595-958-0 (ISBN)
Presentation
2016-06-07, L1, Drottning Kristinas väg 30, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20160525

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

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