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Comparative framework for activity-travel diary collection systems
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0002-0916-0188
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.ORCID iD: 0000-0001-7124-7164
2015 (English)In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, IEEE conference proceedings, 2015, p. 251-258Conference paper, Published paper (Refereed)
Resource type
Text
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. p. 251-258
Keywords [en]
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: urn:nbn:se:kth:diva-181605DOI: 10.1109/MTITS.2015.7223264ISI: 000380478600032Scopus ID: 2-s2.0-84951044698ISBN: 9789633131428 (print)OAI: oai:DiVA.org:kth-181605DiVA, id: diva2:912373
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: 2022-06-23Bibliographically 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. p. 88
Series
TRITA-SOM, ISSN 1653-6126 ; 2016-05
Keywords
travel diary automation, trajectory segmentation, travel data collection, travel diary collection system evaluation and comparison
National Category
Transport Systems and Logistics Computer Sciences 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: 2022-06-22Bibliographically approved
2. 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
Keywords
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: 2022-06-26Bibliographically approved

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Prelipcean, AdrianGidófalvi, GyözöSusilo, Yusak

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