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Longest common subsequences: Identifying the stability of individuals’ travel patterns
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, 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. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, 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)Manuscript (preprint) (Other academic)
Abstract [en]

There is a strong consensus in the travel behaviour research community that the one day travel diary collection is insufficient to understand the finer aspects of behaviour that transcend attributes such as average trip length, duration, travel modes, etc. While a large body research was done on exploring the spatial, temporal and spatio-temporal travel behavioural patterns, the sequential aspect of behaviour is seldom studied. The consensus of the few papers that have studied travel behaviour variability from a sequential perspective has been to use edit distance and compute the costs of transforming one day of travel activities into another. While useful, this approach generates difficult to understand metrics since it does not directly extract (sub)sequences but computes penalties. This paper provides an alternative for investigating the sequential aspect of travel behaviour that makes use of longest common subsequences to extract the activities that are common to multiple days and / or users. The proposed methodology provides indexes for measuring the inter- and intra-personal stability of a given user base and its usefulness is proved in a case study on travel diaries collected from 51 users for a period of 7 days.

Keywords [en]
longest common subsequence, multiple day travel patterns, travel behaviour
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science; Computer Science; Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-227260OAI: oai:DiVA.org:kth-227260DiVA, id: diva2:1203973
Note

QC 20180508

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-05-08Bibliographically 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
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: 2018-05-07Bibliographically approved

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