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Capturing travel entities to facilitate travel behaviour analysis: A case study on generating travel diaries from trajectories fused with accelerometer readings
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics. (Geoinformatics)ORCID iD: 0000-0002-0916-0188
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 [en]
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: urn:nbn:se:kth:diva-187491ISBN: 978-91-7595-958-0 (print)OAI: oai:DiVA.org:kth-187491DiVA: diva2:930577
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
List of papers
1. Mobility Collector
Open this publication in new window or tab >>Mobility Collector
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.

Keyword
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
Identifiers
urn:nbn:se:kth:diva-161529 (URN)10.1080/17489725.2014.973917 (DOI)2-s2.0-84914664420 (Scopus ID)
Conference
Mobile Ghent 2013, Ghent, Belgium, 23-25 October, 2013
Projects
SPOT
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20150312

Available from: 2015-03-12 Created: 2015-03-12 Last updated: 2017-12-04Bibliographically approved
2. Comparative framework for activity-travel diary collection systems
Open this publication in new window or tab >>Comparative framework for activity-travel diary collection systems
2015 (English)In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, IEEE conference proceedings, 2015, 251-258 p.Conference paper, Published paper (Refereed)
Abstract [en]

The needs for cheaper and less intrusive ways to collect activity-travel diaries led scientist to pursue new technologies, e.g., positioning technologies like GPS. While a fully, reliable and widely accepted automatic activity-travel diary collection system is yet to be developed, scientists have presented systems that automate parts of an activity-travel diary collection. In the advent of automated systems, it is important to discuss how to analyse the potential of such systems and how to compare different activity-travel diary collection systems. To achieve this objective, this paper introduces a parallel survey design and a comparison framework for collection systems. The framework can be used as a development tool to optimise system design, to report and monitor progress of different system designs, to objectively weigh benefits in decision making, and to automate systematic analysis. In particular, the framework can be used as a comparison tool to reveal the qualitative difference in the data gathered using different collection systems. To achieve this, the framework defines: 1) a number of activity-travel diary measurement entities (trips and triplegs), entity attributes (e.g., trip purpose, origin / destination, etc.), 2) similarity functions between instances of the same entities, and 3) spatial and temporal quality indices to establish a notion of ground truth. The utility of the proposed framework is demonstrated by analysing the results of a trial survey where data is collected via two activity-travel collection systems: a web-based system (PP) and a smartphone-app-based system (MEILI). PP was collected for one day period and MEILI was used for one week period (with one day overlapping). The results show that half of the trips are captured by both systems, while each system roughly captures the same number of trips as the other. The strengths and weaknesses of MEILI are analysed using the framework on the entire week dataset.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keyword
activity-travel diary collection, comparison of collection methods, ground truth, spatial and temporal quality indices, Automation, Decision making, Intelligent systems, Intelligent vehicle highway systems, Surveys, Systems analysis, Collection methods, Origin destination, Positioning technologies, Qualitative differences, Similarity functions, Systematic analysis, Temporal quality, Transportation
National Category
Embedded Systems
Identifiers
urn:nbn:se:kth:diva-181605 (URN)10.1109/MTITS.2015.7223264 (DOI)000380478600032 ()2-s2.0-84951044698 (Scopus ID)9789633131428 (ISBN)
Conference
International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, 3 June 2015 through 5 June 2015
Note

QC 20160316

Available from: 2016-03-16 Created: 2016-02-02 Last updated: 2016-11-03Bibliographically approved
3. Measures of transport mode segmentation of trajectories
Open this publication in new window or tab >>Measures of transport mode segmentation of trajectories
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
Keyword
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:nbn:se:kth:diva-184485 (URN)10.1080/13658816.2015.1137297 (DOI)000378064300005 ()2-s2.0-84958541974 (Scopus ID)
Note

QC 20160509

Available from: 2016-03-31 Created: 2016-03-31 Last updated: 2017-11-30Bibliographically approved
4. Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, Sweden
Open this publication in new window or tab >>Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, Sweden
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper describes the lessons learned from the trial of MEILI, a smartphone based semi-automatic activity-travel diarycollection system, in Stockholm city, Sweden. The design of the system, together with state-of-the-art improvements of different elements of the tool, are presented before and after the trial to better illustrate the improvements based on the lessons learned. During the trial, both MEILI and a paper-based diary captured about 65% of the total number of detected trips, but only about half of the trips were captured by both systems. The unmatchable trips are partly due to different activity declaration and system specific destination specification, i.e., a verbose specification of address in the paper-and-pencil survey and a point of interest selection / declaration in MEILI. In terms of subjective appreciation, the user experiences vary greatly between the different participants in the pilot. Presumably, this is mainly due to different level of IT-knowledge of the respondents, but also due to the occasionally non-uniform behaviour of the location collection service caused by hardware and / or software difficulties. Based on these inputs, further web and support system improvements have been implemented for future trials.

Keyword
smartphone based survey; semi-automatic travel diary collection system; activity-travel diary; destination and purpose inference; travel mode detection
National Category
Transport Systems and Logistics
Research subject
Transport Science; Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-187489 (URN)
Projects
TRV 2014/10422
Note

QC 20160525

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

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Prelipcean, Adrian Corneliu

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